mirror of
https://github.com/aljazceru/Auto-GPT.git
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Added setup command refactored memstore (#5555)
* forge - restructured memstore * Stopped setup from being ran as defualt when running an agent
This commit is contained in:
Swifty
@@ -1 +1,2 @@
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from .memstore import MemStore
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from .chroma_memstore import ChromaMemStore
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160
autogpts/forge/forge/sdk/memory/chroma_memstore.py
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160
autogpts/forge/forge/sdk/memory/chroma_memstore.py
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@@ -0,0 +1,160 @@
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from .memstore import MemStore
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import chromadb
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from chromadb.config import Settings
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import hashlib
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class ChromaMemStore:
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"""
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A class used to represent a Memory Store
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"""
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def __init__(self, store_path: str):
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"""
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Initialize the MemStore with a given store path.
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Args:
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store_path (str): The path to the store.
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"""
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self.client = chromadb.PersistentClient(
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path=store_path, settings=Settings(anonymized_telemetry=False)
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)
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def add(self, task_id: str, document: str, metadatas: dict) -> None:
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"""
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Add a document to the MemStore.
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Args:
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task_id (str): The ID of the task.
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document (str): The document to be added.
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metadatas (dict): The metadata of the document.
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"""
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doc_id = hashlib.sha256(document.encode()).hexdigest()[:20]
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collection = self.client.get_or_create_collection(task_id)
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collection.add(documents=[document], metadatas=[metadatas], ids=[doc_id])
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def query(
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self,
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task_id: str,
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query: str,
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filters: dict = None,
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document_search: dict = None,
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) -> dict:
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"""
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Query the MemStore.
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Args:
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task_id (str): The ID of the task.
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query (str): The query string.
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filters (dict, optional): The filters to be applied. Defaults to None.
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search_string (str, optional): The search string. Defaults to None.
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Returns:
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dict: The query results.
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"""
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collection = self.client.get_or_create_collection(task_id)
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kwargs = {
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"query_texts": [query],
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"n_results": 10,
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}
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if filters:
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kwargs["where"] = filters
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if document_search:
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kwargs["where_document"] = document_search
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return collection.query(**kwargs)
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def get(self, task_id: str, doc_ids: list = None, filters: dict = None) -> dict:
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"""
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Get documents from the MemStore.
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Args:
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task_id (str): The ID of the task.
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doc_ids (list, optional): The IDs of the documents to be retrieved. Defaults to None.
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filters (dict, optional): The filters to be applied. Defaults to None.
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Returns:
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dict: The retrieved documents.
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"""
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collection = self.client.get_or_create_collection(task_id)
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kwargs = {}
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if doc_ids:
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kwargs["ids"] = doc_ids
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if filters:
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kwargs["where"] = filters
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return collection.get(**kwargs)
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def update(self, task_id: str, doc_ids: list, documents: list, metadatas: list):
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"""
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Update documents in the MemStore.
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Args:
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task_id (str): The ID of the task.
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doc_ids (list): The IDs of the documents to be updated.
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documents (list): The updated documents.
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metadatas (list): The updated metadata.
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"""
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collection = self.client.get_or_create_collection(task_id)
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collection.update(ids=doc_ids, documents=documents, metadatas=metadatas)
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def delete(self, task_id: str, doc_id: str):
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"""
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Delete a document from the MemStore.
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Args:
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task_id (str): The ID of the task.
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doc_id (str): The ID of the document to be deleted.
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"""
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collection = self.client.get_or_create_collection(task_id)
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collection.delete(ids=[doc_id])
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if __name__ == "__main__":
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print("#############################################")
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# Initialize MemStore
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mem = ChromaMemStore(".agent_mem_store")
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# Test add function
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task_id = "test_task"
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document = "This is a another new test document."
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metadatas = {"metadata": "test_metadata"}
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mem.add(task_id, document, metadatas)
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task_id = "test_task"
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document = "The quick brown fox jumps over the lazy dog."
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metadatas = {"metadata": "test_metadata"}
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mem.add(task_id, document, metadatas)
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task_id = "test_task"
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document = "AI is a new technology that will change the world."
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metadatas = {"timestamp": 1623936000}
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mem.add(task_id, document, metadatas)
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doc_id = hashlib.sha256(document.encode()).hexdigest()[:20]
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# Test query function
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query = "test"
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filters = {"metadata": {"$eq": "test"}}
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search_string = {"$contains": "test"}
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doc_ids = [doc_id]
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documents = ["This is an updated test document."]
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updated_metadatas = {"metadata": "updated_test_metadata"}
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print("Query:")
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print(mem.query(task_id, query))
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# Test get function
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print("Get:")
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print(mem.get(task_id))
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# Test update function
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print("Update:")
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print(mem.update(task_id, doc_ids, documents, updated_metadatas))
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print("Delete:")
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# Test delete function
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print(mem.delete(task_id, doc_ids[0]))
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@@ -149,158 +149,3 @@ class MemStore(abc.ABC):
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@abc.abstractmethod
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def delete(self, collection_name: str, doc_id: str):
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pass
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class ChromaMemStore(MemStore):
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"""
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A class used to represent a Memory Store
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"""
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def __init__(self, store_path: str):
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"""
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Initialize the MemStore with a given store path.
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Args:
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store_path (str): The path to the store.
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"""
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self.client = chromadb.PersistentClient(
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path=store_path, settings=Settings(anonymized_telemetry=False)
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)
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def add(self, task_id: str, document: str, metadatas: dict) -> None:
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"""
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Add a document to the MemStore.
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Args:
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task_id (str): The ID of the task.
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document (str): The document to be added.
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metadatas (dict): The metadata of the document.
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"""
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doc_id = hashlib.sha256(document.encode()).hexdigest()[:20]
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collection = self.client.get_or_create_collection(task_id)
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collection.add(documents=[document], metadatas=[metadatas], ids=[doc_id])
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def query(
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self,
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task_id: str,
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query: str,
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filters: dict = None,
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document_search: dict = None,
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) -> dict:
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"""
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Query the MemStore.
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Args:
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task_id (str): The ID of the task.
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query (str): The query string.
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filters (dict, optional): The filters to be applied. Defaults to None.
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search_string (str, optional): The search string. Defaults to None.
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Returns:
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dict: The query results.
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"""
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collection = self.client.get_or_create_collection(task_id)
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kwargs = {
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"query_texts": [query],
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"n_results": 10,
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}
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if filters:
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kwargs["where"] = filters
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if document_search:
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kwargs["where_document"] = document_search
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return collection.query(**kwargs)
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def get(self, task_id: str, doc_ids: list = None, filters: dict = None) -> dict:
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"""
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Get documents from the MemStore.
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Args:
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task_id (str): The ID of the task.
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doc_ids (list, optional): The IDs of the documents to be retrieved. Defaults to None.
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filters (dict, optional): The filters to be applied. Defaults to None.
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Returns:
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dict: The retrieved documents.
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"""
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collection = self.client.get_or_create_collection(task_id)
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kwargs = {}
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if doc_ids:
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kwargs["ids"] = doc_ids
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if filters:
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kwargs["where"] = filters
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return collection.get(**kwargs)
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def update(self, task_id: str, doc_ids: list, documents: list, metadatas: list):
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"""
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Update documents in the MemStore.
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Args:
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task_id (str): The ID of the task.
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doc_ids (list): The IDs of the documents to be updated.
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documents (list): The updated documents.
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metadatas (list): The updated metadata.
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"""
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collection = self.client.get_or_create_collection(task_id)
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collection.update(ids=doc_ids, documents=documents, metadatas=metadatas)
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def delete(self, task_id: str, doc_id: str):
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"""
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Delete a document from the MemStore.
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Args:
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task_id (str): The ID of the task.
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doc_id (str): The ID of the document to be deleted.
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"""
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collection = self.client.get_or_create_collection(task_id)
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collection.delete(ids=[doc_id])
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if __name__ == "__main__":
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print("#############################################")
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# Initialize MemStore
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mem = MemStore(".agent_mem_store")
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# Test add function
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task_id = "test_task"
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document = "This is a another new test document."
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metadatas = {"metadata": "test_metadata"}
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mem.add(task_id, document, metadatas)
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task_id = "test_task"
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document = "The quick brown fox jumps over the lazy dog."
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metadatas = {"metadata": "test_metadata"}
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mem.add(task_id, document, metadatas)
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task_id = "test_task"
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document = "AI is a new technology that will change the world."
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metadatas = {"timestamp": 1623936000}
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mem.add(task_id, document, metadatas)
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doc_id = hashlib.sha256(document.encode()).hexdigest()[:20]
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# Test query function
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query = "test"
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filters = {"metadata": {"$eq": "test"}}
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search_string = {"$contains": "test"}
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doc_ids = [doc_id]
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documents = ["This is an updated test document."]
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updated_metadatas = {"metadata": "updated_test_metadata"}
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print("Query:")
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print(mem.query(task_id, query))
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# Test get function
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print("Get:")
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print(mem.get(task_id))
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# Test update function
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print("Update:")
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print(mem.update(task_id, doc_ids, documents, updated_metadatas))
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print("Delete:")
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# Test delete function
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print(mem.delete(task_id, doc_ids[0]))
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8
cli.py
8
cli.py
@@ -258,7 +258,8 @@ def create(agent_name):
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@agent.command()
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@click.argument("agent_name")
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def start(agent_name):
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@click.option("--setup", is_flag=True, help="Rebuilds your poetry env")
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def start(agent_name, setup):
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"""Start agent command"""
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import os
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import subprocess
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@@ -269,8 +270,9 @@ def start(agent_name):
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run_bench_command = os.path.join(agent_dir, "run_benchmark")
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if os.path.exists(agent_dir) and os.path.isfile(run_command) and os.path.isfile(run_bench_command):
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os.chdir(agent_dir)
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setup_process = subprocess.Popen(["./setup"], cwd=agent_dir)
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setup_process.wait()
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if setup:
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setup_process = subprocess.Popen(["./setup"], cwd=agent_dir)
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setup_process.wait()
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subprocess.Popen(["./run_benchmark", "serve"], cwd=agent_dir)
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click.echo(f"Benchmark Server starting please wait...")
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subprocess.Popen(["./run"], cwd=agent_dir)
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