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Feat/memory base (#1444)

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* byom - short/entity memory

* better

* rm uneeded

* fix text

* use context

* rm dep and sync

* type check fix

* fixed test using new cassete

* fixing types

* fixed types

* fix types

* fixed types

* fixing types

* fix type

* cassette update

* just mock the return of short term mem

* remove print

* try catch block

* added docs

* dding error handling here
This commit is contained in:
Lorenze Jay
2024-10-17 09:19:33 -07:00
committed by GitHub
parent 67f55bae2c
commit 6d20ba70a1
14 changed files with 241 additions and 558 deletions
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77
docs/concepts/memory.mdx
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@@ -34,7 +34,7 @@ By default, the memory system is disabled, and you can ensure it is active by se
The memory will use OpenAI embeddings by default, but you can change it by setting `embedder` to a different model.
It's also possible to initialize the memory instance with your own instance.
The 'embedder' only applies to **Short-Term Memory** which uses Chroma for RAG using the EmbedChain package.
The 'embedder' only applies to **Short-Term Memory** which uses Chroma for RAG.
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.
@@ -105,12 +105,9 @@ my_crew = Crew(
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "openai",
"config": {
"model": 'text-embedding-3-small'
}
}
embedder=embedding_functions.OpenAIEmbeddingFunction(
api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
)
)
```
@@ -125,14 +122,10 @@ my_crew = Crew(
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "google",
"config": {
"model": 'models/embedding-001',
"task_type": "retrieval_document",
"title": "Embeddings for Embedchain"
}
}
embedder=embedding_functions.OpenAIEmbeddingFunction(
api_key=os.getenv("OPENAI_API_KEY"),
model_name="text-embedding-ada-002"
)
)
```
@@ -147,30 +140,13 @@ my_crew = Crew(
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "azure_openai",
"config": {
"model": 'text-embedding-ada-002',
"deployment_name": "your_embedding_model_deployment_name"
}
}
)
```
### Using GPT4ALL embeddings
```python Code
from crewai import Crew, Agent, Task, Process
my_crew = Crew(
agents=[...],
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "gpt4all"
}
embedder=embedding_functions.OpenAIEmbeddingFunction(
api_key="YOUR_API_KEY",
api_base="YOUR_API_BASE_PATH",
api_type="azure",
api_version="YOUR_API_VERSION",
model_name="text-embedding-3-small"
)
)
```
@@ -185,12 +161,12 @@ my_crew = Crew(
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "vertexai",
"config": {
"model": 'textembedding-gecko'
}
}
embedder=embedding_functions.GoogleVertexEmbeddingFunction(
project_id="YOUR_PROJECT_ID",
region="YOUR_REGION",
api_key="YOUR_API_KEY",
model_name="textembedding-gecko"
)
)
```
@@ -205,13 +181,10 @@ my_crew = Crew(
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "cohere",
"config": {
"model": "embed-english-v3.0",
"vector_dimension": 1024
}
}
embedder=embedding_functions.CohereEmbeddingFunction(
api_key=YOUR_API_KEY,
model_name="<model_name>"
)
)
```