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Code Issues Actions 1 Packages Projects Releases Wiki Activity

Removing LangChain and Rebuilding Executor (#1322)

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* rebuilding executor

* removing langchain

* Making all tests good

* fixing types and adding ability for nor using system prompts

* improving types

* pleasing the types gods

* pleasing the types gods

* fixing parser, tools and executor

* making sure all tests pass

* final pass

* fixing type

* Updating Docs

* preparing to cut new version
This commit is contained in:
João Moura
2024-09-16 14:14:04 -03:00
committed by GitHub
parent 322780a5f3
commit e77442cf34
177 changed files with 27272 additions and 1618561 deletions
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155
docs/core-concepts/Memory.md
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@@ -4,16 +4,17 @@ description: Leveraging memory systems in the crewAI framework to enhance agent
---
## Introduction to Memory Systems in crewAI
!!! note "Enhancing Agent Intelligence"
The crewAI framework introduces a sophisticated memory system designed to significantly enhance the capabilities of AI agents. This system comprises short-term memory, long-term memory, entity memory, and contextual memory, each serving a unique purpose in aiding agents to remember, reason, and learn from past interactions.
## Memory System Components
| Component | Description |
| :------------------- | :----------------------------------------------------------- |
| **Short-Term Memory**| Temporarily stores recent interactions and outcomes, enabling agents to recall and utilize information relevant to their current context during the current executions. |
| **Long-Term Memory** | Preserves valuable insights and learnings from past executions, allowing agents to build and refine their knowledge over time. So Agents can remember what they did right and wrong across multiple executions |
| **Entity Memory** | Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. |
| Component | Description |
| :------------------- | :---------------------------------------------------------------------------------------------------------------------- |
| **Short-Term Memory**| Temporarily stores recent interactions and outcomes using `RAG`, enabling agents to recall and utilize information relevant to their current context during the current executions.|
| **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. |
## How Memory Systems Empower Agents
@@ -27,12 +28,12 @@ description: Leveraging memory systems in the crewAI framework to enhance agent
## Implementing Memory in Your Crew
When configuring a crew, you can enable and customize each memory component to suit the crew's objectives and the nature of tasks it will perform.
By default, the memory system is disabled, and you can ensure it is active by setting `memory=True` in the crew configuration. The memory will use OpenAI Embeddings by default, but you can change it by setting `embedder` to a different model.
By default, the memory system is disabled, and you can ensure it is active by setting `memory=True` in the crew configuration. The memory will use OpenAI embeddings by default, but you can change it by setting `embedder` to a different model.
The 'embedder' only applies to **Short-Term Memory** which uses Chroma for RAG using EmbedChain package.
The **Long-Term Memory** uses SQLLite3 to store task results. Currently, there is no way to override these storage implementations.
The data storage files are saved into a platform specific location found using the appdirs package
and the name of the project which can be overridden using the **CREWAI_STORAGE_DIR** environment variable.
The 'embedder' only applies to **Short-Term Memory** which uses Chroma for RAG using the EmbedChain package.
The **Long-Term Memory** uses SQLite3 to store task results. Currently, there is no way to override these storage implementations.
The data storage files are saved into a platform-specific location found using the appdirs package,
and the name of the project can be overridden using the **CREWAI_STORAGE_DIR** environment variable.
### Example: Configuring Memory for a Crew
@@ -56,17 +57,17 @@ my_crew = Crew(
from crewai import Crew, Agent, Task, Process
my_crew = Crew(
agents=[...],
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "openai",
"config":{
"model": 'text-embedding-3-small'
}
}
agents=[...],
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "openai",
"config": {
"model": 'text-embedding-3-small'
}
}
)
```
@@ -75,19 +76,19 @@ my_crew = Crew(
from crewai import Crew, Agent, Task, Process
my_crew = Crew(
agents=[...],
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "google",
"config":{
"model": 'models/embedding-001',
"task_type": "retrieval_document",
"title": "Embeddings for Embedchain"
}
}
agents=[...],
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "google",
"config": {
"model": 'models/embedding-001',
"task_type": "retrieval_document",
"title": "Embeddings for Embedchain"
}
}
)
```
@@ -96,18 +97,18 @@ my_crew = Crew(
from crewai import Crew, Agent, Task, Process
my_crew = Crew(
agents=[...],
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "azure_openai",
"config":{
"model": 'text-embedding-ada-002',
"deployment_name": "your_embedding_model_deployment_name"
}
}
agents=[...],
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "azure_openai",
"config": {
"model": 'text-embedding-ada-002',
"deployment_name": "your_embedding_model_deployment_name"
}
}
)
```
@@ -116,14 +117,14 @@ my_crew = Crew(
from crewai import Crew, Agent, Task, Process
my_crew = Crew(
agents=[...],
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "gpt4all"
}
agents=[...],
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "gpt4all"
}
)
```
@@ -132,17 +133,17 @@ my_crew = Crew(
from crewai import Crew, Agent, Task, Process
my_crew = Crew(
agents=[...],
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "vertexai",
"config":{
"model": 'textembedding-gecko'
}
}
agents=[...],
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "vertexai",
"config": {
"model": 'textembedding-gecko'
}
}
)
```
@@ -151,18 +152,18 @@ my_crew = Crew(
from crewai import Crew, Agent, Task, Process
my_crew = Crew(
agents=[...],
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "cohere",
"config":{
"model": "embed-english-v3.0",
"vector_dimension": 1024
}
}
agents=[...],
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "cohere",
"config": {
"model": "embed-english-v3.0",
"vector_dimension": 1024
}
}
)
```