docs: fix long term memory class name in examples (#2049)

* docs: fix long term memory class name in examples

- Replace EnhanceLongTermMemory with LongTermMemory to match actual implementation
- Update code examples to show correct usage
- Fixes #2026

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

* docs: improve memory examples with imports, types and security

- Add proper import statements
- Add type hints for better readability
- Add descriptive comments for each memory type
- Add security considerations section
- Add configuration examples section
- Use environment variables for storage paths

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

* Update memory.mdx

* Update memory.mdx

---------

Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Joe Moura <joao@crewai.com>
Co-authored-by: João Moura <joaomdmoura@gmail.com>
This commit is contained in:
devin-ai-integration[bot]
2025-02-09 16:47:31 -03:00
committed by GitHub
parent e0600e3bb9
commit d6d98ee969

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@@ -58,41 +58,107 @@ my_crew = Crew(
### Example: Use Custom Memory Instances e.g FAISS as the VectorDB
```python Code
from crewai import Crew, Agent, Task, Process
from crewai import Crew, Process
from crewai.memory import LongTermMemory, ShortTermMemory, EntityMemory
from crewai.memory.storage import LTMSQLiteStorage, RAGStorage
from typing import List, Optional
# Assemble your crew with memory capabilities
my_crew = Crew(
agents=[...],
tasks=[...],
process="Process.sequential",
memory=True,
long_term_memory=EnhanceLongTermMemory(
my_crew: Crew = Crew(
agents = [...],
tasks = [...],
process = Process.sequential,
memory = True,
# Long-term memory for persistent storage across sessions
long_term_memory = LongTermMemory(
storage=LTMSQLiteStorage(
db_path="/my_data_dir/my_crew1/long_term_memory_storage.db"
db_path="/my_crew1/long_term_memory_storage.db"
)
),
short_term_memory=EnhanceShortTermMemory(
storage=CustomRAGStorage(
crew_name="my_crew",
storage_type="short_term",
data_dir="//my_data_dir",
model=embedder["model"],
dimension=embedder["dimension"],
# Short-term memory for current context using RAG
short_term_memory = ShortTermMemory(
storage = RAGStorage(
embedder_config={
"provider": "openai",
"config": {
"model": 'text-embedding-3-small'
}
},
type="short_term",
path="/my_crew1/"
)
),
),
entity_memory=EnhanceEntityMemory(
storage=CustomRAGStorage(
crew_name="my_crew",
storage_type="entities",
data_dir="//my_data_dir",
model=embedder["model"],
dimension=embedder["dimension"],
),
# Entity memory for tracking key information about entities
entity_memory = EntityMemory(
storage=RAGStorage(
embedder_config={
"provider": "openai",
"config": {
"model": 'text-embedding-3-small'
}
},
type="short_term",
path="/my_crew1/"
)
),
verbose=True,
)
```
## Security Considerations
When configuring memory storage:
- Use environment variables for storage paths (e.g., `CREWAI_STORAGE_DIR`)
- Never hardcode sensitive information like database credentials
- Consider access permissions for storage directories
- Use relative paths when possible to maintain portability
Example using environment variables:
```python
import os
from crewai import Crew
from crewai.memory import LongTermMemory
from crewai.memory.storage import LTMSQLiteStorage
# Configure storage path using environment variable
storage_path = os.getenv("CREWAI_STORAGE_DIR", "./storage")
crew = Crew(
memory=True,
long_term_memory=LongTermMemory(
storage=LTMSQLiteStorage(
db_path="{storage_path}/memory.db".format(storage_path=storage_path)
)
)
)
```
## Configuration Examples
### Basic Memory Configuration
```python
from crewai import Crew
from crewai.memory import LongTermMemory
# Simple memory configuration
crew = Crew(memory=True) # Uses default storage locations
```
### Custom Storage Configuration
```python
from crewai import Crew
from crewai.memory import LongTermMemory
from crewai.memory.storage import LTMSQLiteStorage
# Configure custom storage paths
crew = Crew(
memory=True,
long_term_memory=LongTermMemory(
storage=LTMSQLiteStorage(db_path="./memory.db")
)
)
```
## Integrating Mem0 for Enhanced User Memory
[Mem0](https://mem0.ai/) is a self-improving memory layer for LLM applications, enabling personalized AI experiences.