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Knowledge (#1567)

Browse Source 
* initial knowledge

* WIP

* Adding core knowledge sources

* Improve types and better support for file paths

* added additional sources

* fix linting

* update yaml to include optional deps

* adding in lorenze feedback

* ensure embeddings are persisted

* improvements all around Knowledge class

* return this

* properly reset memory

* properly reset memory+knowledge

* consolodation and improvements

* linted

* cleanup rm unused embedder

* fix test

* fix duplicate

* generating cassettes for knowledge test

* updated default embedder

* None embedder to use default on pipeline cloning

* improvements

* fixed text_file_knowledge

* mypysrc fixes

* type check fixes

* added extra cassette

* just mocks

* linted

* mock knowledge query to not spin up db

* linted

* verbose run

* put a flag

* fix

* adding docs

* better docs

* improvements from review

* more docs

* linted

* rm print

* more fixes

* clearer docs

* added docstrings and type hints for cli

---------

Co-authored-by: João Moura <joaomdmoura@gmail.com>
Co-authored-by: Lorenze Jay <lorenzejaytech@gmail.com>
This commit is contained in:
Brandon Hancock (bhancock_ai)
2024-11-20 18:40:08 -05:00
committed by GitHub
parent fde1ee45f9
commit 14a36d3f5e
37 changed files with 2302 additions and 266 deletions
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4
.github/workflows/tests.yml vendored
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@@ -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

75
docs/concepts/knowledge.mdx Normal file
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@@ -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"}},
},
)
```

10
pyproject.toml
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@@ -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]

14
src/crewai/__init__.py
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@@ -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 @@ warnings.filterwarnings(
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",
]

15
src/crewai/agent.py
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@@ -11,8 +11,8 @@ from crewai.agents.crew_agent_executor import CrewAgentExecutor
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 @@ 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}"
tools = tools or self.tools or []
self.create_agent_executor(tools=tools, task=task)

14
src/crewai/cli/cli.py
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@@ -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)

15
src/crewai/cli/reset_memories_command.py
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@@ -5,9 +5,17 @@ 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.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)

14
src/crewai/crew.py
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@@ -27,6 +27,7 @@ from crewai.llm import LLM
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 @@ class Crew(BaseModel):
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."""

0
src/crewai/knowledge/__init__.py Normal file
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0
src/crewai/knowledge/embedder/__init__.py Normal file
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55
src/crewai/knowledge/embedder/base_embedder.py Normal file
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@@ -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

93
src/crewai/knowledge/embedder/fastembed.py Normal file
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@@ -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)

54
src/crewai/knowledge/knowledge.py Normal file
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@@ -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

0
src/crewai/knowledge/source/__init__.py Normal file
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36
src/crewai/knowledge/source/base_file_knowledge_source.py Normal file
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@@ -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)

48
src/crewai/knowledge/source/base_knowledge_source.py Normal file
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@@ -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)

44
src/crewai/knowledge/source/csv_knowledge_source.py Normal file
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@@ -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)
]

56
src/crewai/knowledge/source/excel_knowledge_source.py Normal file
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@@ -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)
]

54
src/crewai/knowledge/source/json_knowledge_source.py Normal file
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@@ -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)
]

54
src/crewai/knowledge/source/pdf_knowledge_source.py Normal file
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@@ -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)
]

33
src/crewai/knowledge/source/string_knowledge_source.py Normal file
View File

@@ -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)
]

35
src/crewai/knowledge/source/text_file_knowledge_source.py Normal file
View File

@@ -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)
]

0
src/crewai/knowledge/storage/__init__.py Normal file
View File

29
src/crewai/knowledge/storage/base_knowledge_storage.py Normal file
View File

@@ -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

132
src/crewai/knowledge/storage/knowledge_storage.py Normal file
View File

@@ -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()
)

134
src/crewai/memory/storage/rag_storage.py
View File

@@ -4,13 +4,12 @@ import logging
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 @@ class RAGStorage(BaseRAGStorage):
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

2
src/crewai/utilities/__init__.py
View File

@@ -10,6 +10,7 @@ from .rpm_controller import RPMController
from .exceptions.context_window_exceeding_exception import (
LLMContextLengthExceededException,
)
from .embedding_configurator import EmbeddingConfigurator
__all__ = [
"Converter",
@@ -23,4 +24,5 @@ __all__ = [
"RPMController",
"YamlParser",
"LLMContextLengthExceededException",
"EmbeddingConfigurator",
]

1
src/crewai/utilities/constants.py
View File

@@ -1,2 +1,3 @@
TRAINING_DATA_FILE = "training_data.pkl"
TRAINED_AGENTS_DATA_FILE = "trained_agents_data.pkl"
DEFAULT_SCORE_THRESHOLD = 0.35

183
src/crewai/utilities/embedding_configurator.py Normal file
View File

@@ -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()

42
tests/agent_test.py
View File

@@ -10,10 +10,11 @@ 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 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()

115
tests/cassettes/test_agent_with_knowledge_sources.yaml Normal file
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not a summary.\n\nBegin! This is VERY important to you, use the tools available
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- max-age=31536000; includeSubDomains; preload
x-ratelimit-limit-requests:
- '30000'
x-ratelimit-limit-tokens:
- '150000000'
x-ratelimit-remaining-requests:
- '29999'
x-ratelimit-remaining-tokens:
- '149999790'
x-ratelimit-reset-requests:
- 2ms
x-ratelimit-reset-tokens:
- 0s
x-request-id:
- req_8f1622677c64913753a595f679596614
status:
code: 200
message: OK
version: 1

232
tests/cassettes/test_kickoff_for_each_error_handling.yaml Normal file
View File

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"""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()

2
tests/pipeline/test_pipeline.py
View File

@@ -38,6 +38,7 @@ def mock_crew_factory():
crew = MockCrew()
crew.name = name
crew.knowledge = None
task_output = TaskOutput(
description="Test task", raw="Task output", agent="Test Agent"
@@ -67,6 +68,7 @@ def mock_crew_factory():
crew.process = Process.sequential
crew.config = None
crew.cache = True
crew.embedder = None
# Add non-empty agents and tasks
mock_agent = MagicMock(spec=Agent)

428
uv.lock generated
View File

@@ -638,6 +638,18 @@ dependencies = [
agentops = [
{ name = "agentops" },
]
fastembed = [
{ name = "fastembed" },
]
openpyxl = [
{ name = "openpyxl" },
]
pandas = [
{ name = "pandas" },
]
pdfplumber = [
{ name = "pdfplumber" },
]
mem0 = [
{ name = "mem0ai" },
]
@@ -674,6 +686,7 @@ requires-dist = [
{ name = "click", specifier = ">=8.1.7" },
{ name = "crewai-tools", specifier = ">=0.14.0" },
{ name = "crewai-tools", marker = "extra == 'tools'", specifier = ">=0.14.0" },
{ name = "fastembed", marker = "extra == 'fastembed'", specifier = ">=0.4.1" },
{ name = "instructor", specifier = ">=1.3.3" },
{ name = "json-repair", specifier = ">=0.25.2" },
{ name = "jsonref", specifier = ">=1.1.0" },
@@ -681,9 +694,12 @@ requires-dist = [
{ name = "litellm", specifier = ">=1.44.22" },
{ name = "mem0ai", marker = "extra == 'mem0'", specifier = ">=0.1.29" },
{ name = "openai", specifier = ">=1.13.3" },
{ name = "openpyxl", marker = "extra == 'openpyxl'", specifier = ">=3.1.5" },
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{ name = "opentelemetry-sdk", specifier = ">=1.22.0" },
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{ name = "pdfplumber", marker = "extra == 'pdfplumber'", specifier = ">=0.11.4" },
{ name = "pydantic", specifier = ">=2.4.2" },
{ name = "python-dotenv", specifier = ">=1.0.0" },
{ name = "pyvis", specifier = ">=0.3.2" },
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version = "2.0.0"
source = { registry = "https://pypi.org/simple" }
sdist = { url = "https://files.pythonhosted.org/packages/d3/38/af70d7ab1ae9d4da450eeec1fa3918940a5fafb9055e934af8d6eb0c2313/et_xmlfile-2.0.0.tar.gz", hash = "sha256:dab3f4764309081ce75662649be815c4c9081e88f0837825f90fd28317d4da54", size = 17234 }
wheels = [
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version = "0.4.1"
source = { registry = "https://pypi.org/simple" }
dependencies = [
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{ name = "loguru" },
{ name = "mmh3" },
{ name = "numpy" },
{ name = "onnx" },
{ name = "onnxruntime" },
{ name = "pillow" },
{ name = "py-rust-stemmers" },
{ name = "requests" },
{ name = "tokenizers" },
{ name = "tqdm" },
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sdist = { url = "https://files.pythonhosted.org/packages/0c/75/0883d15b54016fa99a32cc333182bf862025bf0983daac417a1beabb53bf/fastembed-0.4.1.tar.gz", hash = "sha256:d5dcfffc3554dca48caf16eec35e38c20544c58e396a5d215f238d40c8442718", size = 40461 }
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[[package]]
name = "loguru"
version = "0.7.2"
source = { registry = "https://pypi.org/simple" }
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sdist = { url = "https://files.pythonhosted.org/packages/9e/30/d87a423766b24db416a46e9335b9602b054a72b96a88a241f2b09b560fa8/loguru-0.7.2.tar.gz", hash = "sha256:e671a53522515f34fd406340ee968cb9ecafbc4b36c679da03c18fd8d0bd51ac", size = 145103 }
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