andre/crewAI
1
0
Fork 0
mirror of https://github.com/crewAIInc/crewAI.git synced 2026-01-09 08:08:32 +00:00
Code Issues Actions 1 Packages Projects Releases Wiki Activity

Add support for retrieving user preferences and memories using Mem0 (#1209)

Browse Source 
* Integrate Mem0

* Update src/crewai/memory/contextual/contextual_memory.py

Co-authored-by: Deshraj Yadav <deshraj@gatech.edu>

* pending commit for _fetch_user_memories

* update poetry.lock

* fixes mypy issues

* fix mypy checks

* New fixes for user_id

* remove memory_provider

* handle memory_provider

* checks for memory_config

* add mem0 to dependency

* Update pyproject.toml

Co-authored-by: Deshraj Yadav <deshraj@gatech.edu>

* update docs

* update doc

* bump mem0 version

* fix api error msg and mypy issue

* mypy fix

* resolve comments

* fix memory usage without mem0

* mem0 version bump

* lazy import mem0

---------

Co-authored-by: Deshraj Yadav <deshraj@gatech.edu>
Co-authored-by: João Moura <joaomdmoura@gmail.com>
Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>
This commit is contained in:
Dev Khant
2024-11-15 00:29:24 +05:30
committed by GitHub
parent 9285ebf8a2
commit e70bc94ab6
17 changed files with 619 additions and 34 deletions
Download Patch File Download Diff File Expand all files Collapse all files

3
docs/concepts/crews.mdx
View File

@@ -22,7 +22,8 @@ A crew in crewAI represents a collaborative group of agents working together to
| **Max RPM** _(optional)_ | `max_rpm` | Maximum requests per minute the crew adheres to during execution. Defaults to `None`. |
| **Language** _(optional)_ | `language` | Language used for the crew, defaults to English. |
| **Language File** _(optional)_ | `language_file` | Path to the language file to be used for the crew. |
| **Memory** _(optional)_ | `memory` | Utilized for storing execution memories (short-term, long-term, entity memory). Defaults to `False`. |
| **Memory** _(optional)_ | `memory` | Utilized for storing execution memories (short-term, long-term, entity memory). |
| **Memory Config** _(optional)_ | `memory_config` | Configuration for the memory provider to be used by the crew. |
| **Cache** _(optional)_ | `cache` | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`. |
| **Embedder** _(optional)_ | `embedder` | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`. |
| **Full Output** _(optional)_ | `full_output` | Whether the crew should return the full output with all tasks outputs or just the final output. Defaults to `False`. |

42
docs/concepts/memory.mdx
View File

@@ -18,6 +18,7 @@ reason, and learn from past interactions.
| **Long-Term Memory** | Preserves valuable insights and learnings from past executions, allowing agents to build and refine their knowledge over time. |
| **Entity Memory** | Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. Uses `RAG` for storing entity information. |
| **Contextual Memory**| Maintains the context of interactions by combining `ShortTermMemory`, `LongTermMemory`, and `EntityMemory`, aiding in the coherence and relevance of agent responses over a sequence of tasks or a conversation. |
| **User Memory** | Stores user-specific information and preferences, enhancing personalization and user experience. |
## How Memory Systems Empower Agents
@@ -92,6 +93,47 @@ my_crew = Crew(
)
```
## Integrating Mem0 for Enhanced User Memory
[Mem0](https://mem0.ai/) is a self-improving memory layer for LLM applications, enabling personalized AI experiences.
To include user-specific memory you can get your API key [here](https://app.mem0.ai/dashboard/api-keys) and refer the [docs](https://docs.mem0.ai/platform/quickstart#4-1-create-memories) for adding user preferences.
```python Code
import os
from crewai import Crew, Process
from mem0 import MemoryClient
# Set environment variables for Mem0
os.environ["MEM0_API_KEY"] = "m0-xx"
# Step 1: Record preferences based on past conversation or user input
client = MemoryClient()
messages = [
{"role": "user", "content": "Hi there! I'm planning a vacation and could use some advice."},
{"role": "assistant", "content": "Hello! I'd be happy to help with your vacation planning. What kind of destination do you prefer?"},
{"role": "user", "content": "I am more of a beach person than a mountain person."},
{"role": "assistant", "content": "That's interesting. Do you like hotels or Airbnb?"},
{"role": "user", "content": "I like Airbnb more."},
]
client.add(messages, user_id="john")
# Step 2: Create a Crew with User Memory
crew = Crew(
agents=[...],
tasks=[...],
verbose=True,
process=Process.sequential,
memory=True,
memory_config={
"provider": "mem0",
"config": {"user_id": "john"},
},
)
```
## Additional Embedding Providers