[MRG] add lancedb as memory

mle/cli.py

@@ -10,7 +10,6 @@
 
 import mle
 from mle.server import app
-from mle.utils import Memory
 import mle.workflow as workflow
 from mle.utils.system import (
     get_config,
@@ -278,9 +277,6 @@ def new(name):
             'integration': {},
         }, outfile, default_flow_style=False)
 
-    # init the memory
-    Memory(project_dir)
-
 
 @cli.command()
 @click.option('--reset', is_flag=True, help='Reset the integration')

mle/utils/memory.py

@@ -1,7 +1,10 @@
 import uuid
 import os.path
 from datetime import datetime
-from typing import List, Dict
+from typing import List, Dict, Optional
+
+import lancedb
+from lancedb.embeddings import get_registry
 
 import chromadb
 from chromadb.utils import embedding_functions
@@ -11,7 +14,8 @@
 chromadb.logger.setLevel(chromadb.logging.ERROR)
 
 
-class Memory:
+class ChromaDBMemory:
+
     def __init__(
             self,
             project_path: str,
@@ -152,3 +156,144 @@ def reset(self):
         Notice: You may need to set the environment variable `ALLOW_RESET` to `TRUE` to enable this function.
         """
         self.client.reset()
+
+
+class LanceDBMemory:
+
+    def __init__(
+        self,
+        project_path: str,
+    ):
+        """
+        Memory: A base class for memory and external knowledge management.
+        Args:
+            project_path: the path to store the data.
+        """
+        self.db_name = '.mle'
+        self.table_name = 'memory'
+        self.client = lancedb.connect(uri=self.db_name)
+
+        config = get_config(project_path)
+        if config["platform"] == "OpenAI":
+            self.text_embedding = get_registry().get("openai").create(api_key=config["api_key"])
+        else:
+            raise NotImplementedError
+
+    def add(
+        self,
+        texts: List[str],
+        metadata: Optional[List[Dict]] = None,
+        table_name: Optional[str] = None,
+        ids: Optional[List[str]] = None,
+    ) -> List[str]:
+        """
+        Adds a list of text items to the specified memory table in the database.
+
+        Args:
+            texts (List[str]): A list of text strings to be added.
+            metadata (Optional[List[Dict]]): A list of metadata to be added.
+            table_name (Optional[str]): The name of the table to add data to. Defaults to self.table_name.
+            ids (Optional[List[str]]): A list of unique IDs for the text items.
+                If not provided, random UUIDs are generated.
+
+        Returns:
+            List[str]: A list of IDs associated with the added text items.
+        """
+        if isinstance(texts, str):
+            texts = (texts, )
+
+        if metadata is None:
+            metadata = [None, ] * len(texts)
+        elif isinstance(metadata, dict):
+            metadata = (metadata, )
+        else:
+            assert len(texts) == len(metadata)
+
+        embeds = self.text_embedding.compute_source_embeddings(texts)
+
+        table_name = table_name or self.table_name
+        ids = ids or [str(uuid.uuid4()) for _ in range(len(texts))]
+
+        data = [
+            {
+                "vector": embed,
+                "text": text,
+                "id": idx,
+                "metadata": meta,
+            } for idx, text, embed, meta in zip(ids, texts, embeds, metadata)
+        ]
+
+        if table_name not in self.client.table_names():
+            self.client.create_table(table_name, data=data)
+        else:
+            self.client.open_table(table_name).add(data=data)
+
+        return ids
+
+    def query(self, query_texts: List[str], table_name: Optional[str] = None, n_results: int = 5) -> List[List[dict]]:
+        """
+        Queries the specified memory table for similar text embeddings.
+
+        Args:
+            query_texts (List[str]): A list of query text strings.
+            table_name (Optional[str]): The name of the table to query. Defaults to self.table_name.
+            n_results (int): The maximum number of results to retrieve per query. Default is 5.
+
+        Returns:
+            List[List[dict]]: A list of results for each query text, each result being a dictionary with
+            keys such as "vector", "text", and "id".
+        """
+        table_name = table_name or self.table_name
+        table = self.client.open_table(table_name)
+        query_embeds = self.text_embedding.compute_source_embeddings(query_texts)
+
+        results = [table.search(query).limit(n_results).to_list() for query in query_embeds]
+        return results
+
+    def delete(self, record_id: str, table_name: Optional[str] = None) -> bool:
+        """
+        Deletes a record from the specified memory table.
+
+        Args:
+            record_id (str): The ID of the record to delete.
+            table_name (Optional[str]): The name of the table to delete the record from. Defaults to self.table_name.
+
+        Returns:
+            bool: True if the deletion was successful, False otherwise.
+        """
+        table_name = table_name or self.table_name
+        table = self.client.open_table(table_name)
+        return table.delete(f"id = '{record_id}'")
+
+    def drop(self, table_name: Optional[str] = None) -> bool:
+        """
+        Drops (deletes) the specified memory table.
+
+        Args:
+            table_name (Optional[str]): The name of the table to delete. Defaults to self.table_name.
+
+        Returns:
+            bool: True if the table was successfully dropped, False otherwise.
+        """
+        table_name = table_name or self.table_name
+        return self.client.drop_table(table_name)
+
+    def count(self, table_name: Optional[str] = None) -> int:
+        """
+        Counts the number of records in the specified memory table.
+
+        Args:
+            table_name (Optional[str]): The name of the table to count records in. Defaults to self.table_name.
+
+        Returns:
+            int: The number of records in the table.
+        """
+        table_name = table_name or self.table_name
+        table = self.client.open_table(table_name)
+        return table.count_rows()
+
+    def reset(self) -> None:
+        """
+        Resets the memory by dropping the default memory table.
+        """
+        self.drop()

requirements.txt

@@ -21,3 +21,4 @@ bottleneck~=1.4.0
 google-api-python-client~=2.143.0
 google-auth-httplib2~=0.2.0
 google-auth-oauthlib~=1.2.1
+lancedb~=0.15.0