diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 04f6693d03..76ef2a611a 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -26,7 +26,7 @@ jobs: run: uv python install 3.11.9 - name: Install the project - run: uv sync --dev + run: uv sync --dev --all-extras - name: Run tests - run: uv run pytest tests + run: uv run pytest tests -vv diff --git a/docs/concepts/knowledge.mdx b/docs/concepts/knowledge.mdx new file mode 100644 index 0000000000..2afb1b5689 --- /dev/null +++ b/docs/concepts/knowledge.mdx @@ -0,0 +1,75 @@ +--- +title: Knowledge +description: What is knowledge in CrewAI and how to use it. +icon: book +--- + +# Using Knowledge in CrewAI + +## Introduction + +The Knowledge class in CrewAI provides a powerful way to manage and query knowledge sources for your AI agents. This guide will show you how to implement knowledge management in your CrewAI projects. +Additionally, we have specific tools for generate knowledge sources for strings, text files, PDF's, and Spreadsheets. You can expand on any source type by extending the `KnowledgeSource` class. + +## Basic Implementation + +Here's a simple example of how to use the Knowledge class: + +```python +from crewai import Agent, Task, Crew, Process, LLM +from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource + +# Create a knowledge source +content = "Users name is John. He is 30 years old and lives in San Francisco." +string_source = StringKnowledgeSource( + content=content, metadata={"preference": "personal"} +) + + +llm = LLM(model="gpt-4o-mini", temperature=0) + # Create an agent with the knowledge store +agent = Agent( + role="About User", + goal="You know everything about the user.", + backstory="""You are a master at understanding people and their preferences.""", + verbose=True, + allow_delegation=False, + llm=llm, +) +task = Task( + description="Answer the following questions about the user: {question}", + expected_output="An answer to the question.", + agent=agent, +) + +crew = Crew( + agents=[agent], + 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. +) + +result = crew.kickoff(inputs={"question": "What city does John live in and how old is he?"}) +``` + + +## Embedder Configuration + +You can also configure the embedder for the knowledge store. This is useful if you want to use a different embedder for the knowledge store than the one used for the agents. + +```python +... +string_source = StringKnowledgeSource( + content="Users name is John. He is 30 years old and lives in San Francisco.", + metadata={"preference": "personal"} +) +crew = Crew( + ... + knowledge={ + "sources": [string_source], + "metadata": {"preference": "personal"}, + "embedder_config": {"provider": "openai", "config": {"model": "text-embedding-3-small"}}, + }, +) +``` diff --git a/pyproject.toml b/pyproject.toml index 5955baf330..2dbc00e24e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -39,6 +39,16 @@ Repository = "https://github.com/crewAIInc/crewAI" [project.optional-dependencies] tools = ["crewai-tools>=0.14.0"] agentops = ["agentops>=0.3.0"] +fastembed = ["fastembed>=0.4.1"] +pdfplumber = [ + "pdfplumber>=0.11.4", +] +pandas = [ + "pandas>=2.2.3", +] +openpyxl = [ + "openpyxl>=3.1.5", +] mem0 = ["mem0ai>=0.1.29"] [tool.uv] diff --git a/src/crewai/__init__.py b/src/crewai/__init__.py index fbad09663f..6cfa381de0 100644 --- a/src/crewai/__init__.py +++ b/src/crewai/__init__.py @@ -1,7 +1,9 @@ import warnings + from crewai.agent import Agent from crewai.crew import Crew from crewai.flow.flow import Flow +from crewai.knowledge.knowledge import Knowledge from crewai.llm import LLM from crewai.pipeline import Pipeline from crewai.process import Process @@ -15,4 +17,14 @@ module="pydantic.main", ) __version__ = "0.80.0" -__all__ = ["Agent", "Crew", "Process", "Task", "Pipeline", "Router", "LLM", "Flow"] +__all__ = [ + "Agent", + "Crew", + "Process", + "Task", + "Pipeline", + "Router", + "LLM", + "Flow", + "Knowledge", +] diff --git a/src/crewai/agent.py b/src/crewai/agent.py index 4e9a0685f5..d17cbbdfed 100644 --- a/src/crewai/agent.py +++ b/src/crewai/agent.py @@ -11,8 +11,8 @@ from crewai.cli.constants import ENV_VARS from crewai.llm import LLM from crewai.memory.contextual.contextual_memory import ContextualMemory -from crewai.tools.agent_tools.agent_tools import AgentTools from crewai.tools import BaseTool +from crewai.tools.agent_tools.agent_tools import AgentTools from crewai.utilities import Converter, Prompts from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE from crewai.utilities.token_counter_callback import TokenCalcHandler @@ -52,6 +52,7 @@ class Agent(BaseAgent): role: The role of the agent. goal: The objective of the agent. backstory: The backstory of the agent. + knowledge: The knowledge base of the agent. config: Dict representation of agent configuration. llm: The language model that will run the agent. function_calling_llm: The language model that will handle the tool calling for this agent, it overrides the crew function_calling_llm. @@ -272,6 +273,18 @@ def execute_task( 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}" + tools = tools or self.tools or [] self.create_agent_executor(tools=tools, task=task) diff --git a/src/crewai/cli/cli.py b/src/crewai/cli/cli.py index 0f43ff3f47..600eb6142b 100644 --- a/src/crewai/cli/cli.py +++ b/src/crewai/cli/cli.py @@ -136,6 +136,7 @@ def log_tasks_outputs() -> None: @click.option("-l", "--long", is_flag=True, help="Reset LONG TERM memory") @click.option("-s", "--short", is_flag=True, help="Reset SHORT TERM memory") @click.option("-e", "--entities", is_flag=True, help="Reset ENTITIES memory") +@click.option("-kn", "--knowledge", is_flag=True, help="Reset KNOWLEDGE storage") @click.option( "-k", "--kickoff-outputs", @@ -143,17 +144,24 @@ def log_tasks_outputs() -> None: help="Reset LATEST KICKOFF TASK OUTPUTS", ) @click.option("-a", "--all", is_flag=True, help="Reset ALL memories") -def reset_memories(long, short, entities, kickoff_outputs, all): +def reset_memories( + long: bool, + short: bool, + entities: bool, + knowledge: bool, + kickoff_outputs: bool, + all: bool, +) -> None: """ Reset the crew memories (long, short, entity, latest_crew_kickoff_ouputs). This will delete all the data saved. """ try: - if not all and not (long or short or entities or kickoff_outputs): + if not all and not (long or short or entities or knowledge or kickoff_outputs): click.echo( "Please specify at least one memory type to reset using the appropriate flags." ) return - reset_memories_command(long, short, entities, kickoff_outputs, all) + reset_memories_command(long, short, entities, knowledge, kickoff_outputs, all) except Exception as e: click.echo(f"An error occurred while resetting memories: {e}", err=True) diff --git a/src/crewai/cli/reset_memories_command.py b/src/crewai/cli/reset_memories_command.py index c4808594fa..31624cfc34 100644 --- a/src/crewai/cli/reset_memories_command.py +++ b/src/crewai/cli/reset_memories_command.py @@ -5,9 +5,17 @@ from crewai.memory.long_term.long_term_memory import LongTermMemory from crewai.memory.short_term.short_term_memory import ShortTermMemory from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler +from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage -def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None: +def reset_memories_command( + long, + short, + entity, + knowledge, + kickoff_outputs, + all, +) -> None: """ Reset the crew memories. @@ -17,6 +25,7 @@ def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None: entity (bool): Whether to reset the entity memory. kickoff_outputs (bool): Whether to reset the latest kickoff task outputs. all (bool): Whether to reset all memories. + knowledge (bool): Whether to reset the knowledge. """ try: @@ -25,6 +34,7 @@ def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None: EntityMemory().reset() LongTermMemory().reset() TaskOutputStorageHandler().reset() + KnowledgeStorage().reset() click.echo("All memories have been reset.") else: if long: @@ -40,6 +50,9 @@ def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None: if kickoff_outputs: TaskOutputStorageHandler().reset() click.echo("Latest Kickoff outputs stored has been reset.") + if knowledge: + KnowledgeStorage().reset() + click.echo("Knowledge has been reset.") except subprocess.CalledProcessError as e: click.echo(f"An error occurred while resetting the memories: {e}", err=True) diff --git a/src/crewai/crew.py b/src/crewai/crew.py index 7f55399768..4c3886a3f4 100644 --- a/src/crewai/crew.py +++ b/src/crewai/crew.py @@ -27,6 +27,7 @@ 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.memory.user.user_memory import UserMemory from crewai.process import Process from crewai.task import Task @@ -201,6 +202,10 @@ 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." + ) + @field_validator("id", mode="before") @classmethod @@ -275,6 +280,15 @@ def create_crew_memory(self) -> "Crew": self._user_memory = None return self + @model_validator(mode="after") + def create_crew_knowledge(self) -> "Crew": + if self.knowledge: + 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)}") + return self + @model_validator(mode="after") def check_manager_llm(self): """Validates that the language model is set when using hierarchical process.""" diff --git a/src/crewai/knowledge/__init__.py b/src/crewai/knowledge/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/src/crewai/knowledge/embedder/__init__.py b/src/crewai/knowledge/embedder/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/src/crewai/knowledge/embedder/base_embedder.py b/src/crewai/knowledge/embedder/base_embedder.py new file mode 100644 index 0000000000..c3252bf432 --- /dev/null +++ b/src/crewai/knowledge/embedder/base_embedder.py @@ -0,0 +1,55 @@ +from abc import ABC, abstractmethod +from typing import List + +import numpy as np + + +class BaseEmbedder(ABC): + """ + Abstract base class for text embedding models + """ + + @abstractmethod + def embed_chunks(self, chunks: List[str]) -> np.ndarray: + """ + Generate embeddings for a list of text chunks + + Args: + chunks: List of text chunks to embed + + Returns: + Array of embeddings + """ + pass + + @abstractmethod + def embed_texts(self, texts: List[str]) -> np.ndarray: + """ + Generate embeddings for a list of texts + + Args: + texts: List of texts to embed + + Returns: + Array of embeddings + """ + pass + + @abstractmethod + def embed_text(self, text: str) -> np.ndarray: + """ + Generate embedding for a single text + + Args: + text: Text to embed + + Returns: + Embedding array + """ + pass + + @property + @abstractmethod + def dimension(self) -> int: + """Get the dimension of the embeddings""" + pass diff --git a/src/crewai/knowledge/embedder/fastembed.py b/src/crewai/knowledge/embedder/fastembed.py new file mode 100644 index 0000000000..54db116431 --- /dev/null +++ b/src/crewai/knowledge/embedder/fastembed.py @@ -0,0 +1,93 @@ +from pathlib import Path +from typing import List, Optional, Union + +import numpy as np + +from .base_embedder import BaseEmbedder + +try: + from fastembed_gpu import TextEmbedding # type: ignore + + FASTEMBED_AVAILABLE = True +except ImportError: + try: + from fastembed import TextEmbedding + + FASTEMBED_AVAILABLE = True + except ImportError: + FASTEMBED_AVAILABLE = False + + +class FastEmbed(BaseEmbedder): + """ + A wrapper class for text embedding models using FastEmbed + """ + + def __init__( + self, + model_name: str = "BAAI/bge-small-en-v1.5", + cache_dir: Optional[Union[str, Path]] = None, + ): + """ + Initialize the embedding model + + Args: + model_name: Name of the model to use + cache_dir: Directory to cache the model + gpu: Whether to use GPU acceleration + """ + if not FASTEMBED_AVAILABLE: + raise ImportError( + "FastEmbed is not installed. Please install it with: " + "uv pip install fastembed or uv pip install fastembed-gpu for GPU support" + ) + + self.model = TextEmbedding( + model_name=model_name, + cache_dir=str(cache_dir) if cache_dir else None, + ) + + def embed_chunks(self, chunks: List[str]) -> List[np.ndarray]: + """ + Generate embeddings for a list of text chunks + + Args: + chunks: List of text chunks to embed + + Returns: + List of embeddings + """ + embeddings = list(self.model.embed(chunks)) + return embeddings + + def embed_texts(self, texts: List[str]) -> List[np.ndarray]: + """ + Generate embeddings for a list of texts + + Args: + texts: List of texts to embed + + Returns: + List of embeddings + """ + embeddings = list(self.model.embed(texts)) + return embeddings + + def embed_text(self, text: str) -> np.ndarray: + """ + Generate embedding for a single text + + Args: + text: Text to embed + + Returns: + Embedding array + """ + return self.embed_texts([text])[0] + + @property + def dimension(self) -> int: + """Get the dimension of the embeddings""" + # Generate a test embedding to get dimensions + test_embed = self.embed_text("test") + return len(test_embed) diff --git a/src/crewai/knowledge/knowledge.py b/src/crewai/knowledge/knowledge.py new file mode 100644 index 0000000000..cf2907e670 --- /dev/null +++ b/src/crewai/knowledge/knowledge.py @@ -0,0 +1,54 @@ +import os + +from typing import List, Optional, Dict, Any +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 + + +class Knowledge(BaseModel): + """ + Knowledge is a collection of sources and setup for the vector store to save and query relevant context. + Args: + sources: List[BaseKnowledgeSource] = Field(default_factory=list) + 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 + + def __init__(self, embedder_config: Optional[Dict[str, Any]] = 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", + ) + + def query( + self, query: List[str], limit: int = 3, preference: Optional[str] = None + ) -> List[Dict[str, Any]]: + """ + Query across all knowledge sources to find the most relevant information. + Returns the top_k most relevant chunks. + """ + + results = self.storage.search( + query, + limit, + filter={"preference": preference} if preference else None, + score_threshold=DEFAULT_SCORE_THRESHOLD, + ) + return results diff --git a/src/crewai/knowledge/source/__init__.py b/src/crewai/knowledge/source/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/src/crewai/knowledge/source/base_file_knowledge_source.py b/src/crewai/knowledge/source/base_file_knowledge_source.py new file mode 100644 index 0000000000..b6e3465342 --- /dev/null +++ b/src/crewai/knowledge/source/base_file_knowledge_source.py @@ -0,0 +1,36 @@ +from pathlib import Path +from typing import Union, List + +from pydantic import Field + +from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource +from typing import Dict, Any +from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage + + +class BaseFileKnowledgeSource(BaseKnowledgeSource): + """Base class for knowledge sources that load content from files.""" + + file_path: Union[Path, List[Path]] = Field(...) + content: Dict[Path, str] = Field(init=False, default_factory=dict) + storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage) + + def model_post_init(self, _): + """Post-initialization method to load content.""" + self.content = self.load_content() + + def load_content(self) -> Dict[Path, str]: + """Load and preprocess file content. Should be overridden by subclasses.""" + paths = [self.file_path] if isinstance(self.file_path, Path) else self.file_path + + for path in paths: + if not path.exists(): + raise FileNotFoundError(f"File not found: {path}") + if not path.is_file(): + raise ValueError(f"Path is not a file: {path}") + return {} + + def save_documents(self, metadata: Dict[str, Any]): + """Save the documents to the storage.""" + chunk_metadatas = [metadata.copy() for _ in self.chunks] + self.storage.save(self.chunks, chunk_metadatas) diff --git a/src/crewai/knowledge/source/base_knowledge_source.py b/src/crewai/knowledge/source/base_knowledge_source.py new file mode 100644 index 0000000000..bb4c69cf39 --- /dev/null +++ b/src/crewai/knowledge/source/base_knowledge_source.py @@ -0,0 +1,48 @@ +from abc import ABC, abstractmethod +from typing import List, Dict, Any + +import numpy as np +from pydantic import BaseModel, ConfigDict, Field + +from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage + + +class BaseKnowledgeSource(BaseModel, ABC): + """Abstract base class for knowledge sources.""" + + chunk_size: int = 4000 + chunk_overlap: int = 200 + chunks: List[str] = Field(default_factory=list) + chunk_embeddings: List[np.ndarray] = Field(default_factory=list) + + model_config = ConfigDict(arbitrary_types_allowed=True) + storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage) + metadata: Dict[str, Any] = Field(default_factory=dict) + + @abstractmethod + def load_content(self) -> Dict[Any, str]: + """Load and preprocess content from the source.""" + pass + + @abstractmethod + def add(self) -> None: + """Process content, chunk it, compute embeddings, and save them.""" + pass + + def get_embeddings(self) -> List[np.ndarray]: + """Return the list of embeddings for the chunks.""" + return self.chunk_embeddings + + def _chunk_text(self, text: str) -> List[str]: + """Utility method to split text into chunks.""" + return [ + text[i : i + self.chunk_size] + for i in range(0, len(text), self.chunk_size - self.chunk_overlap) + ] + + def save_documents(self, metadata: Dict[str, Any]): + """ + Save the documents to the storage. + This method should be called after the chunks and embeddings are generated. + """ + self.storage.save(self.chunks, metadata) diff --git a/src/crewai/knowledge/source/csv_knowledge_source.py b/src/crewai/knowledge/source/csv_knowledge_source.py new file mode 100644 index 0000000000..0946104a46 --- /dev/null +++ b/src/crewai/knowledge/source/csv_knowledge_source.py @@ -0,0 +1,44 @@ +import csv +from typing import Dict, List +from pathlib import Path + +from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource + + +class CSVKnowledgeSource(BaseFileKnowledgeSource): + """A knowledge source that stores and queries CSV file content using embeddings.""" + + def load_content(self) -> Dict[Path, str]: + """Load and preprocess CSV file content.""" + super().load_content() # Validate the file path + + file_path = ( + self.file_path[0] if isinstance(self.file_path, list) else self.file_path + ) + file_path = Path(file_path) if isinstance(file_path, str) else file_path + + with open(file_path, "r", encoding="utf-8") as csvfile: + reader = csv.reader(csvfile) + content = "" + for row in reader: + content += " ".join(row) + "\n" + return {file_path: content} + + def add(self) -> None: + """ + Add CSV file content to the knowledge source, chunk it, compute embeddings, + and save the embeddings. + """ + content_str = ( + str(self.content) if isinstance(self.content, dict) else self.content + ) + new_chunks = self._chunk_text(content_str) + self.chunks.extend(new_chunks) + self.save_documents(metadata=self.metadata) + + def _chunk_text(self, text: str) -> List[str]: + """Utility method to split text into chunks.""" + return [ + text[i : i + self.chunk_size] + for i in range(0, len(text), self.chunk_size - self.chunk_overlap) + ] diff --git a/src/crewai/knowledge/source/excel_knowledge_source.py b/src/crewai/knowledge/source/excel_knowledge_source.py new file mode 100644 index 0000000000..3b5c715146 --- /dev/null +++ b/src/crewai/knowledge/source/excel_knowledge_source.py @@ -0,0 +1,56 @@ +from typing import Dict, List +from pathlib import Path +from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource + + +class ExcelKnowledgeSource(BaseFileKnowledgeSource): + """A knowledge source that stores and queries Excel file content using embeddings.""" + + def load_content(self) -> Dict[Path, str]: + """Load and preprocess Excel file content.""" + super().load_content() # Validate the file path + pd = self._import_dependencies() + + if isinstance(self.file_path, list): + file_path = self.file_path[0] + else: + file_path = self.file_path + + df = pd.read_excel(file_path) + content = df.to_csv(index=False) + return {file_path: content} + + def _import_dependencies(self): + """Dynamically import dependencies.""" + try: + import openpyxl # noqa + import pandas as pd + + return pd + except ImportError as e: + missing_package = str(e).split()[-1] + raise ImportError( + f"{missing_package} is not installed. Please install it with: pip install {missing_package}" + ) + + def add(self) -> None: + """ + Add Excel file content to the knowledge source, chunk it, compute embeddings, + and save the embeddings. + """ + # Convert dictionary values to a single string if content is a dictionary + if isinstance(self.content, dict): + content_str = "\n".join(str(value) for value in self.content.values()) + else: + content_str = str(self.content) + + new_chunks = self._chunk_text(content_str) + self.chunks.extend(new_chunks) + self.save_documents(metadata=self.metadata) + + def _chunk_text(self, text: str) -> List[str]: + """Utility method to split text into chunks.""" + return [ + text[i : i + self.chunk_size] + for i in range(0, len(text), self.chunk_size - self.chunk_overlap) + ] diff --git a/src/crewai/knowledge/source/json_knowledge_source.py b/src/crewai/knowledge/source/json_knowledge_source.py new file mode 100644 index 0000000000..490423a00b --- /dev/null +++ b/src/crewai/knowledge/source/json_knowledge_source.py @@ -0,0 +1,54 @@ +import json +from typing import Any, Dict, List +from pathlib import Path + +from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource + + +class JSONKnowledgeSource(BaseFileKnowledgeSource): + """A knowledge source that stores and queries JSON file content using embeddings.""" + + def load_content(self) -> Dict[Path, str]: + """Load and preprocess JSON file content.""" + super().load_content() # Validate the file path + paths = [self.file_path] if isinstance(self.file_path, Path) else self.file_path + + content: Dict[Path, str] = {} + for path in paths: + with open(path, "r", encoding="utf-8") as json_file: + data = json.load(json_file) + content[path] = self._json_to_text(data) + return content + + def _json_to_text(self, data: Any, level: int = 0) -> str: + """Recursively convert JSON data to a text representation.""" + text = "" + indent = " " * level + if isinstance(data, dict): + for key, value in data.items(): + text += f"{indent}{key}: {self._json_to_text(value, level + 1)}\n" + elif isinstance(data, list): + for item in data: + text += f"{indent}- {self._json_to_text(item, level + 1)}\n" + else: + text += f"{str(data)}" + return text + + def add(self) -> None: + """ + Add JSON file content to the knowledge source, chunk it, compute embeddings, + and save the embeddings. + """ + content_str = ( + str(self.content) if isinstance(self.content, dict) else self.content + ) + new_chunks = self._chunk_text(content_str) + self.chunks.extend(new_chunks) + self.save_documents(metadata=self.metadata) + + def _chunk_text(self, text: str) -> List[str]: + """Utility method to split text into chunks.""" + return [ + text[i : i + self.chunk_size] + for i in range(0, len(text), self.chunk_size - self.chunk_overlap) + ] diff --git a/src/crewai/knowledge/source/pdf_knowledge_source.py b/src/crewai/knowledge/source/pdf_knowledge_source.py new file mode 100644 index 0000000000..623ba30a26 --- /dev/null +++ b/src/crewai/knowledge/source/pdf_knowledge_source.py @@ -0,0 +1,54 @@ +from typing import List, Dict +from pathlib import Path + +from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource + + +class PDFKnowledgeSource(BaseFileKnowledgeSource): + """A knowledge source that stores and queries PDF file content using embeddings.""" + + def load_content(self) -> Dict[Path, str]: + """Load and preprocess PDF file content.""" + super().load_content() # Validate the file paths + pdfplumber = self._import_pdfplumber() + + paths = [self.file_path] if isinstance(self.file_path, Path) else self.file_path + content = {} + + for path in paths: + text = "" + with pdfplumber.open(path) as pdf: + for page in pdf.pages: + page_text = page.extract_text() + if page_text: + text += page_text + "\n" + content[path] = text + return content + + def _import_pdfplumber(self): + """Dynamically import pdfplumber.""" + try: + import pdfplumber + + return pdfplumber + except ImportError: + raise ImportError( + "pdfplumber is not installed. Please install it with: pip install pdfplumber" + ) + + def add(self) -> None: + """ + Add PDF file content to the knowledge source, chunk it, compute embeddings, + and save the embeddings. + """ + for _, text in self.content.items(): + new_chunks = self._chunk_text(text) + self.chunks.extend(new_chunks) + self.save_documents(metadata=self.metadata) + + def _chunk_text(self, text: str) -> List[str]: + """Utility method to split text into chunks.""" + return [ + text[i : i + self.chunk_size] + for i in range(0, len(text), self.chunk_size - self.chunk_overlap) + ] diff --git a/src/crewai/knowledge/source/string_knowledge_source.py b/src/crewai/knowledge/source/string_knowledge_source.py new file mode 100644 index 0000000000..d4c22e3c18 --- /dev/null +++ b/src/crewai/knowledge/source/string_knowledge_source.py @@ -0,0 +1,33 @@ +from typing import List + +from pydantic import Field + +from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource + + +class StringKnowledgeSource(BaseKnowledgeSource): + """A knowledge source that stores and queries plain text content using embeddings.""" + + content: str = Field(...) + + def model_post_init(self, _): + """Post-initialization method to validate content.""" + self.load_content() + + def load_content(self): + """Validate string content.""" + if not isinstance(self.content, str): + raise ValueError("StringKnowledgeSource only accepts string content") + + def add(self) -> None: + """Add string content to the knowledge source, chunk it, compute embeddings, and save them.""" + new_chunks = self._chunk_text(self.content) + self.chunks.extend(new_chunks) + self.save_documents(metadata=self.metadata) + + def _chunk_text(self, text: str) -> List[str]: + """Utility method to split text into chunks.""" + return [ + text[i : i + self.chunk_size] + for i in range(0, len(text), self.chunk_size - self.chunk_overlap) + ] diff --git a/src/crewai/knowledge/source/text_file_knowledge_source.py b/src/crewai/knowledge/source/text_file_knowledge_source.py new file mode 100644 index 0000000000..640db4ef9c --- /dev/null +++ b/src/crewai/knowledge/source/text_file_knowledge_source.py @@ -0,0 +1,35 @@ +from typing import Dict, List +from pathlib import Path + +from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource + + +class TextFileKnowledgeSource(BaseFileKnowledgeSource): + """A knowledge source that stores and queries text file content using embeddings.""" + + def load_content(self) -> Dict[Path, str]: + """Load and preprocess text file content.""" + super().load_content() + paths = [self.file_path] if isinstance(self.file_path, Path) else self.file_path + content = {} + for path in paths: + with path.open("r", encoding="utf-8") as f: + content[path] = f.read() # type: ignore + return content + + def add(self) -> None: + """ + Add text file content to the knowledge source, chunk it, compute embeddings, + and save the embeddings. + """ + for _, text in self.content.items(): + new_chunks = self._chunk_text(text) + self.chunks.extend(new_chunks) + self.save_documents(metadata=self.metadata) + + def _chunk_text(self, text: str) -> List[str]: + """Utility method to split text into chunks.""" + return [ + text[i : i + self.chunk_size] + for i in range(0, len(text), self.chunk_size - self.chunk_overlap) + ] diff --git a/src/crewai/knowledge/storage/__init__.py b/src/crewai/knowledge/storage/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/src/crewai/knowledge/storage/base_knowledge_storage.py b/src/crewai/knowledge/storage/base_knowledge_storage.py new file mode 100644 index 0000000000..78d370e04b --- /dev/null +++ b/src/crewai/knowledge/storage/base_knowledge_storage.py @@ -0,0 +1,29 @@ +from abc import ABC, abstractmethod +from typing import Dict, Any, List, Optional + + +class BaseKnowledgeStorage(ABC): + """Abstract base class for knowledge storage implementations.""" + + @abstractmethod + def search( + self, + query: List[str], + limit: int = 3, + filter: Optional[dict] = None, + score_threshold: float = 0.35, + ) -> List[Dict[str, Any]]: + """Search for documents in the knowledge base.""" + pass + + @abstractmethod + def save( + self, documents: List[str], metadata: Dict[str, Any] | List[Dict[str, Any]] + ) -> None: + """Save documents to the knowledge base.""" + pass + + @abstractmethod + def reset(self) -> None: + """Reset the knowledge base.""" + pass diff --git a/src/crewai/knowledge/storage/knowledge_storage.py b/src/crewai/knowledge/storage/knowledge_storage.py new file mode 100644 index 0000000000..b3d5ba7505 --- /dev/null +++ b/src/crewai/knowledge/storage/knowledge_storage.py @@ -0,0 +1,132 @@ +import contextlib +import io +import logging +import chromadb +import os +from crewai.utilities.paths import db_storage_path +from typing import Optional, List +from typing import Dict, Any +from crewai.utilities import EmbeddingConfigurator +from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage +import hashlib + + +@contextlib.contextmanager +def suppress_logging( + logger_name="chromadb.segment.impl.vector.local_persistent_hnsw", + level=logging.ERROR, +): + logger = logging.getLogger(logger_name) + original_level = logger.getEffectiveLevel() + logger.setLevel(level) + with ( + contextlib.redirect_stdout(io.StringIO()), + contextlib.redirect_stderr(io.StringIO()), + contextlib.suppress(UserWarning), + ): + yield + logger.setLevel(original_level) + + +class KnowledgeStorage(BaseKnowledgeStorage): + """ + Extends Storage to handle embeddings for memory entries, improving + search efficiency. + """ + + collection: Optional[chromadb.Collection] = None + + def __init__(self, embedder_config: Optional[Dict[str, Any]] = None): + self._initialize_app(embedder_config or {}) + + def search( + self, + query: List[str], + limit: int = 3, + filter: Optional[dict] = None, + score_threshold: float = 0.35, + ) -> List[Dict[str, Any]]: + with suppress_logging(): + if self.collection: + fetched = self.collection.query( + query_texts=query, + n_results=limit, + where=filter, + ) + results = [] + for i in range(len(fetched["ids"][0])): # type: ignore + result = { + "id": fetched["ids"][0][i], # type: ignore + "metadata": fetched["metadatas"][0][i], # type: ignore + "context": fetched["documents"][0][i], # type: ignore + "score": fetched["distances"][0][i], # type: ignore + } + if result["score"] >= score_threshold: # type: ignore + results.append(result) + return results + 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) + + chroma_client = chromadb.PersistentClient( + path=f"{db_storage_path()}/knowledge", + settings=Settings(allow_reset=True), + ) + + self.app = chroma_client + + try: + self.collection = self.app.get_or_create_collection(name="knowledge") + except Exception: + raise Exception("Failed to create or get collection") + + def reset(self): + if self.app: + self.app.reset() + + def save( + self, documents: List[str], metadata: Dict[str, Any] | List[Dict[str, Any]] + ): + if self.collection: + metadatas = [metadata] if isinstance(metadata, dict) else metadata + + ids = [ + hashlib.sha256(doc.encode("utf-8")).hexdigest() for doc in documents + ] + + self.collection.upsert( + documents=documents, + metadatas=metadatas, + ids=ids, + ) + else: + raise Exception("Collection not initialized") + + def _create_default_embedding_function(self): + from chromadb.utils.embedding_functions.openai_embedding_function import ( + OpenAIEmbeddingFunction, + ) + + return OpenAIEmbeddingFunction( + api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small" + ) + + def _set_embedder_config( + self, embedder_config: Optional[Dict[str, Any]] = None + ) -> None: + """Set the embedding configuration for the knowledge storage. + + Args: + embedder_config (Optional[Dict[str, Any]]): Configuration dictionary for the embedder. + If None or empty, defaults to the default embedding function. + """ + self.embedder_config = ( + EmbeddingConfigurator().configure_embedder(embedder_config) + if embedder_config + else self._create_default_embedding_function() + ) diff --git a/src/crewai/memory/storage/rag_storage.py b/src/crewai/memory/storage/rag_storage.py index 7af5fb5544..4023cf5584 100644 --- a/src/crewai/memory/storage/rag_storage.py +++ b/src/crewai/memory/storage/rag_storage.py @@ -4,13 +4,12 @@ import os import shutil import uuid -from typing import Any, Dict, List, Optional, cast -from chromadb import Documents, EmbeddingFunction, Embeddings +from typing import Any, Dict, List, Optional from chromadb.api import ClientAPI -from chromadb.api.types import validate_embedding_function from crewai.memory.storage.base_rag_storage import BaseRAGStorage from crewai.utilities.paths import db_storage_path +from crewai.utilities import EmbeddingConfigurator @contextlib.contextmanager @@ -51,133 +50,8 @@ def __init__(self, type, allow_reset=True, embedder_config=None, crew=None): self._initialize_app() def _set_embedder_config(self): - if self.embedder_config is None: - self.embedder_config = self._create_default_embedding_function() - - if isinstance(self.embedder_config, dict): - provider = self.embedder_config.get("provider") - config = self.embedder_config.get("config", {}) - model_name = config.get("model") - if provider == "openai": - from chromadb.utils.embedding_functions.openai_embedding_function import ( - OpenAIEmbeddingFunction, - ) - - self.embedder_config = OpenAIEmbeddingFunction( - api_key=config.get("api_key") or os.getenv("OPENAI_API_KEY"), - model_name=model_name, - ) - elif provider == "azure": - from chromadb.utils.embedding_functions.openai_embedding_function import ( - OpenAIEmbeddingFunction, - ) - - self.embedder_config = OpenAIEmbeddingFunction( - api_key=config.get("api_key"), - api_base=config.get("api_base"), - api_type=config.get("api_type", "azure"), - api_version=config.get("api_version"), - model_name=model_name, - ) - elif provider == "ollama": - from chromadb.utils.embedding_functions.ollama_embedding_function import ( - OllamaEmbeddingFunction, - ) - - self.embedder_config = OllamaEmbeddingFunction( - url=config.get("url", "http://localhost:11434/api/embeddings"), - model_name=model_name, - ) - elif provider == "vertexai": - from chromadb.utils.embedding_functions.google_embedding_function import ( - GoogleVertexEmbeddingFunction, - ) - - self.embedder_config = GoogleVertexEmbeddingFunction( - model_name=model_name, - api_key=config.get("api_key"), - ) - elif provider == "google": - from chromadb.utils.embedding_functions.google_embedding_function import ( - GoogleGenerativeAiEmbeddingFunction, - ) - - self.embedder_config = GoogleGenerativeAiEmbeddingFunction( - model_name=model_name, - api_key=config.get("api_key"), - ) - elif provider == "cohere": - from chromadb.utils.embedding_functions.cohere_embedding_function import ( - CohereEmbeddingFunction, - ) - - self.embedder_config = CohereEmbeddingFunction( - model_name=model_name, - api_key=config.get("api_key"), - ) - elif provider == "bedrock": - from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import ( - AmazonBedrockEmbeddingFunction, - ) - - self.embedder_config = AmazonBedrockEmbeddingFunction( - session=config.get("session"), - ) - elif provider == "huggingface": - from chromadb.utils.embedding_functions.huggingface_embedding_function import ( - HuggingFaceEmbeddingServer, - ) - - self.embedder_config = HuggingFaceEmbeddingServer( - url=config.get("api_url"), - ) - elif provider == "watson": - try: - import ibm_watsonx_ai.foundation_models as watson_models - from ibm_watsonx_ai import Credentials - from ibm_watsonx_ai.metanames import ( - EmbedTextParamsMetaNames as EmbedParams, - ) - except ImportError as e: - raise ImportError( - "IBM Watson dependencies are not installed. Please install them to use Watson embedding." - ) from e - - class WatsonEmbeddingFunction(EmbeddingFunction): - def __call__(self, input: Documents) -> Embeddings: - if isinstance(input, str): - input = [input] - - embed_params = { - EmbedParams.TRUNCATE_INPUT_TOKENS: 3, - EmbedParams.RETURN_OPTIONS: {"input_text": True}, - } - - embedding = watson_models.Embeddings( - model_id=config.get("model"), - params=embed_params, - credentials=Credentials( - api_key=config.get("api_key"), url=config.get("api_url") - ), - project_id=config.get("project_id"), - ) - - try: - embeddings = embedding.embed_documents(input) - return cast(Embeddings, embeddings) - - except Exception as e: - print("Error during Watson embedding:", e) - raise e - - self.embedder_config = WatsonEmbeddingFunction() - else: - raise Exception( - f"Unsupported embedding provider: {provider}, supported providers: [openai, azure, ollama, vertexai, google, cohere, huggingface, watson]" - ) - else: - validate_embedding_function(self.embedder_config) - self.embedder_config = self.embedder_config + configurator = EmbeddingConfigurator() + self.embedder_config = configurator.configure_embedder(self.embedder_config) def _initialize_app(self): import chromadb diff --git a/src/crewai/utilities/__init__.py b/src/crewai/utilities/__init__.py index 26d35a6ccd..dd6d9fa44f 100644 --- a/src/crewai/utilities/__init__.py +++ b/src/crewai/utilities/__init__.py @@ -10,6 +10,7 @@ from .exceptions.context_window_exceeding_exception import ( LLMContextLengthExceededException, ) +from .embedding_configurator import EmbeddingConfigurator __all__ = [ "Converter", @@ -23,4 +24,5 @@ "RPMController", "YamlParser", "LLMContextLengthExceededException", + "EmbeddingConfigurator", ] diff --git a/src/crewai/utilities/constants.py b/src/crewai/utilities/constants.py index 22cc2ffbe7..59f7899133 100644 --- a/src/crewai/utilities/constants.py +++ b/src/crewai/utilities/constants.py @@ -1,2 +1,3 @@ TRAINING_DATA_FILE = "training_data.pkl" TRAINED_AGENTS_DATA_FILE = "trained_agents_data.pkl" +DEFAULT_SCORE_THRESHOLD = 0.35 diff --git a/src/crewai/utilities/embedding_configurator.py b/src/crewai/utilities/embedding_configurator.py new file mode 100644 index 0000000000..f0f77ffca0 --- /dev/null +++ b/src/crewai/utilities/embedding_configurator.py @@ -0,0 +1,183 @@ +import os +from typing import Any, Dict, cast +from chromadb import EmbeddingFunction, Documents, Embeddings +from chromadb.api.types import validate_embedding_function + + +class EmbeddingConfigurator: + def __init__(self): + self.embedding_functions = { + "openai": self._configure_openai, + "azure": self._configure_azure, + "ollama": self._configure_ollama, + "vertexai": self._configure_vertexai, + "google": self._configure_google, + "cohere": self._configure_cohere, + "bedrock": self._configure_bedrock, + "huggingface": self._configure_huggingface, + "watson": self._configure_watson, + } + + def configure_embedder( + self, + embedder_config: Dict[str, Any] | None = None, + ) -> EmbeddingFunction: + """Configures and returns an embedding function based on the provided config.""" + if embedder_config is None: + return self._create_default_embedding_function() + + provider = embedder_config.get("provider") + config = embedder_config.get("config", {}) + model_name = config.get("model") + + if isinstance(provider, EmbeddingFunction): + try: + validate_embedding_function(provider) + return provider + except Exception as e: + raise ValueError(f"Invalid custom embedding function: {str(e)}") + + if provider not in self.embedding_functions: + raise Exception( + f"Unsupported embedding provider: {provider}, supported providers: {list(self.embedding_functions.keys())}" + ) + + return self.embedding_functions[provider](config, model_name) + + @staticmethod + def _create_default_embedding_function(): + from chromadb.utils.embedding_functions.openai_embedding_function import ( + OpenAIEmbeddingFunction, + ) + + return OpenAIEmbeddingFunction( + api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small" + ) + + @staticmethod + def _configure_openai(config, model_name): + from chromadb.utils.embedding_functions.openai_embedding_function import ( + OpenAIEmbeddingFunction, + ) + + return OpenAIEmbeddingFunction( + api_key=config.get("api_key") or os.getenv("OPENAI_API_KEY"), + model_name=model_name, + ) + + @staticmethod + def _configure_azure(config, model_name): + from chromadb.utils.embedding_functions.openai_embedding_function import ( + OpenAIEmbeddingFunction, + ) + + return OpenAIEmbeddingFunction( + api_key=config.get("api_key"), + api_base=config.get("api_base"), + api_type=config.get("api_type", "azure"), + api_version=config.get("api_version"), + model_name=model_name, + ) + + @staticmethod + def _configure_ollama(config, model_name): + from chromadb.utils.embedding_functions.ollama_embedding_function import ( + OllamaEmbeddingFunction, + ) + + return OllamaEmbeddingFunction( + url=config.get("url", "http://localhost:11434/api/embeddings"), + model_name=model_name, + ) + + @staticmethod + def _configure_vertexai(config, model_name): + from chromadb.utils.embedding_functions.google_embedding_function import ( + GoogleVertexEmbeddingFunction, + ) + + return GoogleVertexEmbeddingFunction( + model_name=model_name, + api_key=config.get("api_key"), + ) + + @staticmethod + def _configure_google(config, model_name): + from chromadb.utils.embedding_functions.google_embedding_function import ( + GoogleGenerativeAiEmbeddingFunction, + ) + + return GoogleGenerativeAiEmbeddingFunction( + model_name=model_name, + api_key=config.get("api_key"), + ) + + @staticmethod + def _configure_cohere(config, model_name): + from chromadb.utils.embedding_functions.cohere_embedding_function import ( + CohereEmbeddingFunction, + ) + + return CohereEmbeddingFunction( + model_name=model_name, + api_key=config.get("api_key"), + ) + + @staticmethod + def _configure_bedrock(config, model_name): + from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import ( + AmazonBedrockEmbeddingFunction, + ) + + return AmazonBedrockEmbeddingFunction( + session=config.get("session"), + ) + + @staticmethod + def _configure_huggingface(config, model_name): + from chromadb.utils.embedding_functions.huggingface_embedding_function import ( + HuggingFaceEmbeddingServer, + ) + + return HuggingFaceEmbeddingServer( + url=config.get("api_url"), + ) + + @staticmethod + def _configure_watson(config, model_name): + try: + import ibm_watsonx_ai.foundation_models as watson_models + from ibm_watsonx_ai import Credentials + from ibm_watsonx_ai.metanames import EmbedTextParamsMetaNames as EmbedParams + except ImportError as e: + raise ImportError( + "IBM Watson dependencies are not installed. Please install them to use Watson embedding." + ) from e + + class WatsonEmbeddingFunction(EmbeddingFunction): + def __call__(self, input: Documents) -> Embeddings: + if isinstance(input, str): + input = [input] + + embed_params = { + EmbedParams.TRUNCATE_INPUT_TOKENS: 3, + EmbedParams.RETURN_OPTIONS: {"input_text": True}, + } + + embedding = watson_models.Embeddings( + model_id=config.get("model"), + params=embed_params, + credentials=Credentials( + api_key=config.get("api_key"), url=config.get("api_url") + ), + project_id=config.get("project_id"), + ) + + try: + embeddings = embedding.embed_documents(input) + return cast(Embeddings, embeddings) + except Exception as e: + print("Error during Watson embedding:", e) + raise e + + return WatsonEmbeddingFunction() diff --git a/tests/agent_test.py b/tests/agent_test.py index c4094d15cf..fb6ab22b81 100644 --- a/tests/agent_test.py +++ b/tests/agent_test.py @@ -10,10 +10,11 @@ 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 import tool from crewai.tools.tool_usage_events import ToolUsageFinished from crewai.utilities import RPMController from crewai.utilities.events import Emitter @@ -1574,3 +1575,42 @@ def test_agent_execute_task_with_ollama(): result = agent.execute_task(task) assert len(result.split(".")) == 2 assert "AI" in result or "artificial intelligence" in result.lower() + + +@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"} + ) + + + 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"} + }] + + 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"), + ) + + # Create a task that requires the agent to use the knowledge + task = Task( + description="What is Brandon's favorite color?", + expected_output="Brandon's favorite color.", + agent=agent, + ) + + crew = Crew(agents=[agent], tasks=[task]) + result = crew.kickoff() + + # Assert that the agent provides the correct information + assert "blue" in result.raw.lower() + diff --git a/tests/cassettes/test_agent_with_knowledge_sources.yaml b/tests/cassettes/test_agent_with_knowledge_sources.yaml new file mode 100644 index 0000000000..d483a19d2c --- /dev/null +++ b/tests/cassettes/test_agent_with_knowledge_sources.yaml @@ -0,0 +1,115 @@ +interactions: +- request: + body: '{"messages": [{"role": "system", "content": "You are Information Agent. + You have access to specific knowledge sources.\nYour personal goal is: Provide + information based on knowledge sources\nTo give my best complete final answer + to the task use the exact following format:\n\nThought: I now can give a great + answer\nFinal Answer: Your final answer must be the great and the most complete + as possible, it must be outcome described.\n\nI MUST use these formats, my job + depends on it!"}, {"role": "user", "content": "\nCurrent Task: What is Brandon''s + favorite color?\n\nThis is the expect criteria for your final answer: Brandon''s + favorite color.\nyou MUST return the actual complete content as the final answer, + not a summary.\n\nBegin! 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Content-Type: + - application/x-protobuf + User-Agent: + - OTel-OTLP-Exporter-Python/1.27.0 + method: POST + uri: https://telemetry.crewai.com:4319/v1/traces + response: + body: + string: "\n\0" + headers: + Content-Length: + - '2' + Content-Type: + - application/x-protobuf + Date: + - Tue, 19 Nov 2024 22:14:39 GMT + status: + code: 200 + message: OK +version: 1 diff --git a/tests/knowledge/__init__.py b/tests/knowledge/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/knowledge/crewai_quickstart.pdf b/tests/knowledge/crewai_quickstart.pdf new file mode 100644 index 0000000000..671baf782c Binary files /dev/null and b/tests/knowledge/crewai_quickstart.pdf differ diff --git a/tests/knowledge/knowledge_test.py b/tests/knowledge/knowledge_test.py new file mode 100644 index 0000000000..4f506ff168 --- /dev/null +++ b/tests/knowledge/knowledge_test.py @@ -0,0 +1,545 @@ +"""Test Knowledge creation and querying functionality.""" + +from pathlib import Path +from unittest.mock import patch + +from crewai.knowledge.source.csv_knowledge_source import CSVKnowledgeSource +from crewai.knowledge.source.excel_knowledge_source import ExcelKnowledgeSource +from crewai.knowledge.source.json_knowledge_source import JSONKnowledgeSource +from crewai.knowledge.source.pdf_knowledge_source import PDFKnowledgeSource +from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource +from crewai.knowledge.source.text_file_knowledge_source import TextFileKnowledgeSource + +import pytest + + +@pytest.fixture(autouse=True) +def mock_vector_db(): + """Mock vector database operations.""" + with patch("crewai.knowledge.storage.knowledge_storage.KnowledgeStorage") as mock: + # Mock the query method to return a predefined response + instance = mock.return_value + instance.query.return_value = [ + { + "context": "Brandon's favorite color is blue and he likes Mexican food.", + "score": 0.9, + } + ] + instance.reset.return_value = None + yield instance + + +@pytest.fixture(autouse=True) +def reset_knowledge_storage(mock_vector_db): + """Fixture to reset knowledge storage before each test.""" + yield + + +def test_single_short_string(mock_vector_db): + # Create a knowledge base with a single short string + content = "Brandon's favorite color is blue and he likes Mexican food." + string_source = StringKnowledgeSource( + content=content, metadata={"preference": "personal"} + ) + mock_vector_db.sources = [string_source] + mock_vector_db.query.return_value = [{"context": content, "score": 0.9}] + # Perform a query + query = "What is Brandon's favorite color?" + results = mock_vector_db.query(query) + + # Assert that the results contain the expected information + assert any("blue" in result["context"].lower() for result in results) + # Verify the mock was called + mock_vector_db.query.assert_called_once() + + +# @pytest.mark.vcr(filter_headers=["authorization"]) +def test_single_2k_character_string(mock_vector_db): + # Create a 2k character string with various facts about Brandon + content = ( + "Brandon is a software engineer who lives in San Francisco. " + "He enjoys hiking and often visits the trails in the Bay Area. " + "Brandon has a pet dog named Max, who is a golden retriever. " + "He loves reading science fiction books, and his favorite author is Isaac Asimov. " + "Brandon's favorite movie is Inception, and he enjoys watching it with his friends. " + "He is also a fan of Mexican cuisine, especially tacos and burritos. " + "Brandon plays the guitar and often performs at local open mic nights. " + "He is learning French and plans to visit Paris next year. " + "Brandon is passionate about technology and often attends tech meetups in the city. " + "He is also interested in AI and machine learning, and he is currently working on a project related to natural language processing. " + "Brandon's favorite color is blue, and he often wears blue shirts. " + "He enjoys cooking and often tries new recipes on weekends. " + "Brandon is a morning person and likes to start his day with a run in the park. " + "He is also a coffee enthusiast and enjoys trying different coffee blends. " + "Brandon is a member of a local book club and enjoys discussing books with fellow members. " + "He is also a fan of board games and often hosts game nights at his place. " + "Brandon is an advocate for environmental conservation and volunteers for local clean-up drives. " + "He is also a mentor for aspiring software developers and enjoys sharing his knowledge with others. " + "Brandon's favorite sport is basketball, and he often plays with his friends on weekends. " + "He is also a fan of the Golden State Warriors and enjoys watching their games. " + ) + string_source = StringKnowledgeSource( + content=content, metadata={"preference": "personal"} + ) + mock_vector_db.sources = [string_source] + mock_vector_db.query.return_value = [{"context": content, "score": 0.9}] + + # Perform a query + query = "What is Brandon's favorite movie?" + results = mock_vector_db.query(query) + + # Assert that the results contain the expected information + assert any("inception" in result["context"].lower() for result in results) + mock_vector_db.query.assert_called_once() + + +def test_multiple_short_strings(mock_vector_db): + # Create multiple short string sources + contents = [ + "Brandon loves hiking.", + "Brandon has a dog named Max.", + "Brandon enjoys painting landscapes.", + ] + string_sources = [ + StringKnowledgeSource(content=content, metadata={"preference": "personal"}) + for content in contents + ] + + # Mock the vector db query response + mock_vector_db.query.return_value = [ + {"context": "Brandon has a dog named Max.", "score": 0.9} + ] + + mock_vector_db.sources = string_sources + + # Perform a query + query = "What is the name of Brandon's pet?" + results = mock_vector_db.query(query) + + # Assert that the correct information is retrieved + assert any("max" in result["context"].lower() for result in results) + # Verify the mock was called + mock_vector_db.query.assert_called_once() + + +def test_multiple_2k_character_strings(mock_vector_db): + # Create multiple 2k character strings with various facts about Brandon + contents = [ + ( + "Brandon is a software engineer who lives in San Francisco. " + "He enjoys hiking and often visits the trails in the Bay Area. " + "Brandon has a pet dog named Max, who is a golden retriever. " + "He loves reading science fiction books, and his favorite author is Isaac Asimov. " + "Brandon's favorite movie is Inception, and he enjoys watching it with his friends. " + "He is also a fan of Mexican cuisine, especially tacos and burritos. " + "Brandon plays the guitar and often performs at local open mic nights. " + "He is learning French and plans to visit Paris next year. " + "Brandon is passionate about technology and often attends tech meetups in the city. " + "He is also interested in AI and machine learning, and he is currently working on a project related to natural language processing. " + "Brandon's favorite color is blue, and he often wears blue shirts. " + "He enjoys cooking and often tries new recipes on weekends. " + "Brandon is a morning person and likes to start his day with a run in the park. " + "He is also a coffee enthusiast and enjoys trying different coffee blends. " + "Brandon is a member of a local book club and enjoys discussing books with fellow members. " + "He is also a fan of board games and often hosts game nights at his place. " + "Brandon is an advocate for environmental conservation and volunteers for local clean-up drives. " + "He is also a mentor for aspiring software developers and enjoys sharing his knowledge with others. " + "Brandon's favorite sport is basketball, and he often plays with his friends on weekends. " + "He is also a fan of the Golden State Warriors and enjoys watching their games. " + ) + * 2, # Repeat to ensure it's 2k characters + ( + "Brandon loves traveling and has visited over 20 countries. " + "He is fluent in Spanish and often practices with his friends. " + "Brandon's favorite city is Barcelona, where he enjoys the architecture and culture. " + "He is a foodie and loves trying new cuisines, with a particular fondness for sushi. " + "Brandon is an avid cyclist and participates in local cycling events. " + "He is also a photographer and enjoys capturing landscapes and cityscapes. " + "Brandon is a tech enthusiast and follows the latest trends in gadgets and software. " + "He is also a fan of virtual reality and owns a VR headset. " + "Brandon's favorite book is 'The Hitchhiker's Guide to the Galaxy'. " + "He enjoys watching documentaries and learning about history and science. " + "Brandon is a coffee lover and has a collection of coffee mugs from different countries. " + "He is also a fan of jazz music and often attends live performances. " + "Brandon is a member of a local running club and participates in marathons. " + "He is also a volunteer at a local animal shelter and helps with dog walking. " + "Brandon's favorite holiday is Christmas, and he enjoys decorating his home. " + "He is also a fan of classic movies and has a collection of DVDs. " + "Brandon is a mentor for young professionals and enjoys giving career advice. " + "He is also a fan of puzzles and enjoys solving them in his free time. " + "Brandon's favorite sport is soccer, and he often plays with his friends. " + "He is also a fan of FC Barcelona and enjoys watching their matches. " + ) + * 2, # Repeat to ensure it's 2k characters + ] + string_sources = [ + StringKnowledgeSource(content=content, metadata={"preference": "personal"}) + for content in contents + ] + + mock_vector_db.sources = string_sources + mock_vector_db.query.return_value = [{"context": contents[1], "score": 0.9}] + + # Perform a query + query = "What is Brandon's favorite book?" + results = mock_vector_db.query(query) + + # Assert that the correct information is retrieved + assert any( + "the hitchhiker's guide to the galaxy" in result["context"].lower() + for result in results + ) + mock_vector_db.query.assert_called_once() + + +def test_single_short_file(mock_vector_db, tmpdir): + # Create a single short text file + content = "Brandon's favorite sport is basketball." + file_path = Path(tmpdir.join("short_file.txt")) + with open(file_path, "w") as f: + f.write(content) + + file_source = TextFileKnowledgeSource( + file_path=file_path, metadata={"preference": "personal"} + ) + mock_vector_db.sources = [file_source] + mock_vector_db.query.return_value = [{"context": content, "score": 0.9}] + # Perform a query + query = "What sport does Brandon like?" + results = mock_vector_db.query(query) + + # Assert that the results contain the expected information + assert any("basketball" in result["context"].lower() for result in results) + mock_vector_db.query.assert_called_once() + + +def test_single_2k_character_file(mock_vector_db, tmpdir): + # Create a single 2k character text file with various facts about Brandon + content = ( + "Brandon is a software engineer who lives in San Francisco. " + "He enjoys hiking and often visits the trails in the Bay Area. " + "Brandon has a pet dog named Max, who is a golden retriever. " + "He loves reading science fiction books, and his favorite author is Isaac Asimov. " + "Brandon's favorite movie is Inception, and he enjoys watching it with his friends. " + "He is also a fan of Mexican cuisine, especially tacos and burritos. " + "Brandon plays the guitar and often performs at local open mic nights. " + "He is learning French and plans to visit Paris next year. " + "Brandon is passionate about technology and often attends tech meetups in the city. " + "He is also interested in AI and machine learning, and he is currently working on a project related to natural language processing. " + "Brandon's favorite color is blue, and he often wears blue shirts. " + "He enjoys cooking and often tries new recipes on weekends. " + "Brandon is a morning person and likes to start his day with a run in the park. " + "He is also a coffee enthusiast and enjoys trying different coffee blends. " + "Brandon is a member of a local book club and enjoys discussing books with fellow members. " + "He is also a fan of board games and often hosts game nights at his place. " + "Brandon is an advocate for environmental conservation and volunteers for local clean-up drives. " + "He is also a mentor for aspiring software developers and enjoys sharing his knowledge with others. " + "Brandon's favorite sport is basketball, and he often plays with his friends on weekends. " + "He is also a fan of the Golden State Warriors and enjoys watching their games. " + ) * 2 # Repeat to ensure it's 2k characters + file_path = Path(tmpdir.join("long_file.txt")) + with open(file_path, "w") as f: + f.write(content) + + file_source = TextFileKnowledgeSource( + file_path=file_path, metadata={"preference": "personal"} + ) + mock_vector_db.sources = [file_source] + mock_vector_db.query.return_value = [{"context": content, "score": 0.9}] + # Perform a query + query = "What is Brandon's favorite movie?" + results = mock_vector_db.query(query) + + # Assert that the results contain the expected information + assert any("inception" in result["context"].lower() for result in results) + mock_vector_db.query.assert_called_once() + + +def test_multiple_short_files(mock_vector_db, tmpdir): + # Create multiple short text files + contents = [ + { + "content": "Brandon works as a software engineer.", + "metadata": {"category": "profession", "source": "occupation"}, + }, + { + "content": "Brandon lives in New York.", + "metadata": {"category": "city", "source": "personal"}, + }, + { + "content": "Brandon enjoys cooking Italian food.", + "metadata": {"category": "hobby", "source": "personal"}, + }, + ] + file_paths = [] + for i, item in enumerate(contents): + file_path = Path(tmpdir.join(f"file_{i}.txt")) + with open(file_path, "w") as f: + f.write(item["content"]) + file_paths.append((file_path, item["metadata"])) + + file_sources = [ + TextFileKnowledgeSource(file_path=path, metadata=metadata) + for path, metadata in file_paths + ] + mock_vector_db.sources = file_sources + mock_vector_db.query.return_value = [ + {"context": "Brandon lives in New York.", "score": 0.9} + ] + # Perform a query + query = "What city does he reside in?" + results = mock_vector_db.query(query) + # Assert that the correct information is retrieved + assert any("new york" in result["context"].lower() for result in results) + mock_vector_db.query.assert_called_once() + + +def test_multiple_2k_character_files(mock_vector_db, tmpdir): + # Create multiple 2k character text files with various facts about Brandon + contents = [ + ( + "Brandon loves traveling and has visited over 20 countries. " + "He is fluent in Spanish and often practices with his friends. " + "Brandon's favorite city is Barcelona, where he enjoys the architecture and culture. " + "He is a foodie and loves trying new cuisines, with a particular fondness for sushi. " + "Brandon is an avid cyclist and participates in local cycling events. " + "He is also a photographer and enjoys capturing landscapes and cityscapes. " + "Brandon is a tech enthusiast and follows the latest trends in gadgets and software. " + "He is also a fan of virtual reality and owns a VR headset. " + "Brandon's favorite book is 'The Hitchhiker's Guide to the Galaxy'. " + "He enjoys watching documentaries and learning about history and science. " + "Brandon is a coffee lover and has a collection of coffee mugs from different countries. " + "He is also a fan of jazz music and often attends live performances. " + "Brandon is a member of a local running club and participates in marathons. " + "He is also a volunteer at a local animal shelter and helps with dog walking. " + "Brandon's favorite holiday is Christmas, and he enjoys decorating his home. " + "He is also a fan of classic movies and has a collection of DVDs. " + "Brandon is a mentor for young professionals and enjoys giving career advice. " + "He is also a fan of puzzles and enjoys solving them in his free time. " + "Brandon's favorite sport is soccer, and he often plays with his friends. " + "He is also a fan of FC Barcelona and enjoys watching their matches. " + ) + * 2, # Repeat to ensure it's 2k characters + ( + "Brandon is a software engineer who lives in San Francisco. " + "He enjoys hiking and often visits the trails in the Bay Area. " + "Brandon has a pet dog named Max, who is a golden retriever. " + "He loves reading science fiction books, and his favorite author is Isaac Asimov. " + "Brandon's favorite movie is Inception, and he enjoys watching it with his friends. " + "He is also a fan of Mexican cuisine, especially tacos and burritos. " + "Brandon plays the guitar and often performs at local open mic nights. " + "He is learning French and plans to visit Paris next year. " + "Brandon is passionate about technology and often attends tech meetups in the city. " + "He is also interested in AI and machine learning, and he is currently working on a project related to natural language processing. " + "Brandon's favorite color is blue, and he often wears blue shirts. " + "He enjoys cooking and often tries new recipes on weekends. " + "Brandon is a morning person and likes to start his day with a run in the park. " + "He is also a coffee enthusiast and enjoys trying different coffee blends. " + "Brandon is a member of a local book club and enjoys discussing books with fellow members. " + "He is also a fan of board games and often hosts game nights at his place. " + "Brandon is an advocate for environmental conservation and volunteers for local clean-up drives. " + "He is also a mentor for aspiring software developers and enjoys sharing his knowledge with others. " + "Brandon's favorite sport is basketball, and he often plays with his friends on weekends. " + "He is also a fan of the Golden State Warriors and enjoys watching their games. " + ) + * 2, # Repeat to ensure it's 2k characters + ] + file_paths = [] + for i, content in enumerate(contents): + file_path = Path(tmpdir.join(f"long_file_{i}.txt")) + with open(file_path, "w") as f: + f.write(content) + file_paths.append(file_path) + + file_sources = [ + TextFileKnowledgeSource(file_path=path, metadata={"preference": "personal"}) + for path in file_paths + ] + mock_vector_db.sources = file_sources + mock_vector_db.query.return_value = [ + { + "context": "Brandon's favorite book is 'The Hitchhiker's Guide to the Galaxy'.", + "score": 0.9, + } + ] + # Perform a query + query = "What is Brandon's favorite book?" + results = mock_vector_db.query(query) + + # Assert that the correct information is retrieved + assert any( + "the hitchhiker's guide to the galaxy" in result["context"].lower() + for result in results + ) + mock_vector_db.query.assert_called_once() + + +@pytest.mark.vcr(filter_headers=["authorization"]) +def test_hybrid_string_and_files(mock_vector_db, tmpdir): + # Create string sources + string_contents = [ + "Brandon is learning French.", + "Brandon visited Paris last summer.", + ] + string_sources = [ + StringKnowledgeSource(content=content, metadata={"preference": "personal"}) + for content in string_contents + ] + + # Create file sources + file_contents = [ + "Brandon prefers tea over coffee.", + "Brandon's favorite book is 'The Alchemist'.", + ] + file_paths = [] + for i, content in enumerate(file_contents): + file_path = Path(tmpdir.join(f"file_{i}.txt")) + with open(file_path, "w") as f: + f.write(content) + file_paths.append(file_path) + + file_sources = [ + TextFileKnowledgeSource(file_path=path, metadata={"preference": "personal"}) + for path in file_paths + ] + + # Combine string and file sources + mock_vector_db.sources = string_sources + file_sources + mock_vector_db.query.return_value = [{"context": file_contents[1], "score": 0.9}] + + # Perform a query + query = "What is Brandon's favorite book?" + results = mock_vector_db.query(query) + + # Assert that the correct information is retrieved + assert any("the alchemist" in result["context"].lower() for result in results) + mock_vector_db.query.assert_called_once() + + +def test_pdf_knowledge_source(mock_vector_db): + # Get the directory of the current file + current_dir = Path(__file__).parent + # Construct the path to the PDF file + pdf_path = current_dir / "crewai_quickstart.pdf" + + # Create a PDFKnowledgeSource + pdf_source = PDFKnowledgeSource( + file_path=pdf_path, metadata={"preference": "personal"} + ) + mock_vector_db.sources = [pdf_source] + mock_vector_db.query.return_value = [ + {"context": "crewai create crew latest-ai-development", "score": 0.9} + ] + + # Perform a query + query = "How do you create a crew?" + results = mock_vector_db.query(query) + + # Assert that the correct information is retrieved + assert any( + "crewai create crew latest-ai-development" in result["context"].lower() + for result in results + ) + mock_vector_db.query.assert_called_once() + + +@pytest.mark.vcr(filter_headers=["authorization"]) +def test_csv_knowledge_source(mock_vector_db, tmpdir): + """Test CSVKnowledgeSource with a simple CSV file.""" + + # Create a CSV file with sample data + csv_content = [ + ["Name", "Age", "City"], + ["Brandon", "30", "New York"], + ["Alice", "25", "Los Angeles"], + ["Bob", "35", "Chicago"], + ] + csv_path = Path(tmpdir.join("data.csv")) + with open(csv_path, "w", encoding="utf-8") as f: + for row in csv_content: + f.write(",".join(row) + "\n") + + # Create a CSVKnowledgeSource + csv_source = CSVKnowledgeSource( + file_path=csv_path, metadata={"preference": "personal"} + ) + mock_vector_db.sources = [csv_source] + mock_vector_db.query.return_value = [ + {"context": "Brandon is 30 years old.", "score": 0.9} + ] + + # Perform a query + query = "How old is Brandon?" + results = mock_vector_db.query(query) + + # Assert that the correct information is retrieved + assert any("30" in result["context"] for result in results) + mock_vector_db.query.assert_called_once() + + +def test_json_knowledge_source(mock_vector_db, tmpdir): + """Test JSONKnowledgeSource with a simple JSON file.""" + + # Create a JSON file with sample data + json_data = { + "people": [ + {"name": "Brandon", "age": 30, "city": "New York"}, + {"name": "Alice", "age": 25, "city": "Los Angeles"}, + {"name": "Bob", "age": 35, "city": "Chicago"}, + ] + } + json_path = Path(tmpdir.join("data.json")) + with open(json_path, "w", encoding="utf-8") as f: + import json + + json.dump(json_data, f) + + # Create a JSONKnowledgeSource + json_source = JSONKnowledgeSource( + file_path=json_path, metadata={"preference": "personal"} + ) + mock_vector_db.sources = [json_source] + mock_vector_db.query.return_value = [ + {"context": "Alice lives in Los Angeles.", "score": 0.9} + ] + + # Perform a query + query = "Where does Alice reside?" + results = mock_vector_db.query(query) + + # Assert that the correct information is retrieved + assert any("los angeles" in result["context"].lower() for result in results) + mock_vector_db.query.assert_called_once() + + +def test_excel_knowledge_source(mock_vector_db, tmpdir): + """Test ExcelKnowledgeSource with a simple Excel file.""" + + # Create an Excel file with sample data + import pandas as pd + + excel_data = { + "Name": ["Brandon", "Alice", "Bob"], + "Age": [30, 25, 35], + "City": ["New York", "Los Angeles", "Chicago"], + } + df = pd.DataFrame(excel_data) + excel_path = Path(tmpdir.join("data.xlsx")) + df.to_excel(excel_path, index=False) + + # Create an ExcelKnowledgeSource + excel_source = ExcelKnowledgeSource( + file_path=excel_path, metadata={"preference": "personal"} + ) + mock_vector_db.sources = [excel_source] + mock_vector_db.query.return_value = [ + {"context": "Brandon is 30 years old.", "score": 0.9} + ] + + # Perform a query + query = "What is Brandon's age?" + results = mock_vector_db.query(query) + + # Assert that the correct information is retrieved + assert any("30" in result["context"] for result in results) + mock_vector_db.query.assert_called_once() diff --git a/tests/pipeline/test_pipeline.py b/tests/pipeline/test_pipeline.py index a08f185c8c..bd0a38183a 100644 --- a/tests/pipeline/test_pipeline.py +++ b/tests/pipeline/test_pipeline.py @@ -38,6 +38,7 @@ def copy( crew = MockCrew() crew.name = name + crew.knowledge = None task_output = TaskOutput( description="Test task", raw="Task output", agent="Test Agent" @@ -67,6 +68,7 @@ async def kickoff_async(inputs=None): crew.process = Process.sequential crew.config = None crew.cache = True + crew.embedder = None # Add non-empty agents and tasks mock_agent = MagicMock(spec=Agent) diff --git a/uv.lock b/uv.lock index ab78a63d0c..a4b545d07a 100644 --- a/uv.lock +++ b/uv.lock @@ -638,6 +638,18 @@ dependencies = [ agentops = [ { name = "agentops" }, ] +fastembed = [ + { name = "fastembed" }, +] +openpyxl = [ + { name = "openpyxl" }, +] +pandas = [ + { name = "pandas" }, +] 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