From 8d5b9ade2b2d2d5debfb29df97a7de2640ed7161 Mon Sep 17 00:00:00 2001 From: moon Date: Tue, 8 Aug 2023 07:39:55 -0700 Subject: [PATCH 1/5] init postgres, half of tests are passing --- agentmemory/__init__.py | 6 +- agentmemory/client.py | 236 +++++++++++++++++++++++++------ agentmemory/helpers.py | 23 +-- agentmemory/main.py | 57 +++----- agentmemory/main_no_comments.py | 212 +++++++++++++++++++++++++++ agentmemory/persistence.py | 4 +- agentmemory/tests/helpers.py | 19 +-- agentmemory/tests/main.py | 3 + agentmemory/tests/persistence.py | 5 - requirements.txt | 5 +- 10 files changed, 463 insertions(+), 107 deletions(-) create mode 100644 agentmemory/main_no_comments.py diff --git a/agentmemory/__init__.py b/agentmemory/__init__.py index 44ecbe7..a72d0ff 100644 --- a/agentmemory/__init__.py +++ b/agentmemory/__init__.py @@ -35,8 +35,7 @@ ) from .client import ( - get_chroma_client, - set_chroma_client, + get_client, ) from .clustering import ( @@ -62,8 +61,7 @@ "export_memory_to_file", "import_json_to_memory", "import_file_to_memory", - "get_chroma_client", - "set_chroma_client", + "get_client", "get_persistent_directory", "create_event", "get_epoch", diff --git a/agentmemory/client.py b/agentmemory/client.py index 0437e53..eb67a74 100644 --- a/agentmemory/client.py +++ b/agentmemory/client.py @@ -1,55 +1,211 @@ +import os +import json + import chromadb +import psycopg2 +from sentence_transformers import SentenceTransformer +from dotenv import load_dotenv -from agentmemory.helpers import debug_log +load_dotenv() -storage_path = "./memory" -client = chromadb.PersistentClient(storage_path) +DEFAULT_CLIENT_TYPE = "CHROMA" +CLIENT_TYPE = os.environ.get("CLIENT_TYPE", DEFAULT_CLIENT_TYPE) +STORAGE_PATH = os.environ.get("STORAGE_PATH", "./memory") +POSTGRES_CONNECTION_STRING = os.environ.get("POSTGRES_CONNECTION_STRING") -def check_client_initialized(): - """ - Check if the client has been initialized, and initialize it if not. +client = None - Example: - >>> check_client_initialized() - """ - if get_chroma_client() is None: - set_chroma_client(chromadb.PersistentClient(storage_path)) +def get_client(client_type=None, *args, **kwargs): + global client + if client_type is None: + client_type = CLIENT_TYPE -def get_chroma_client(): - """ - Get the chromadb client. + if client_type == "POSTGRES": + if POSTGRES_CONNECTION_STRING is None: + raise EnvironmentError( + "Postgres connection string not set in environment variables!" + ) + if client is None: + client = PostgresClient(POSTGRES_CONNECTION_STRING) + return client + else: + if client is None: + client = chromadb.PersistentClient(path=STORAGE_PATH, *args, **kwargs) + return client - Returns: - chromadb.Client: Chromadb client. - Example: - >>> get_chroma_client() - - """ - global client - global storage_path - if client is None: - client = chromadb.PersistentClient(path=storage_path) - return client +class PostgresCollection: + def __init__(self, category, client): + self.category = category + self.client = client + def count(self): + self.client.cur.execute( + f"SELECT COUNT(*) FROM memories WHERE category=%s", (self.category,) + ) + return self.client.cur.fetchone()[0] -def set_chroma_client(data_storage_path=storage_path): - """ - Set the chromadb client. + def add(self, ids, embeddings=None, metadatas=None, documents=None): + if embeddings is None: + for id_, doc, meta in zip(ids, documents, metadatas): + self.client.insert_memory(self.category, doc, meta) + else: + for id_, doc, meta, emb in zip(ids, documents, metadatas, embeddings): + self.client.insert_memory(self.category, doc, meta, emb) - Args: - storage_path (string): The path to the new directory. + def get( + self, + ids=None, + where=None, + limit=None, + offset=None, + where_document=None, + include=["metadatas", "documents"], + ): + query = f"SELECT * FROM memories WHERE category=%s LIMIT %s OFFSET %s" + self.client.cur.execute(query, (self.category, limit, offset)) + rows = self.client.cur.fetchall() + + # Convert rows to list of dictionaries + columns = [desc[0] for desc in self.client.cur.description] + return [dict(zip(columns, row)) for row in rows] - Returns: - None - Example: - >>> set_chroma_client(new_client) - """ - global client - global storage_path - storage_path = data_storage_path - client = chromadb.PersistentClient(storage_path) - debug_log("Set chroma client", {"storage_path": storage_path}, "system") + def peek(self, limit=10): + return self.get(limit=limit) + + def query( + self, + query_embeddings=None, + query_texts=None, + n_results=10, + where=None, + where_document=None, + include=["metadatas", "documents", "distances"], + ): + return self.client.query(self.category, query_texts, n_results) + + def update(self, ids, embeddings=None, metadatas=None, documents=None): + for id_, doc, meta, emb in zip(ids, documents, metadatas, embeddings): + self.client.update(self.category, id_, doc, meta) + + def upsert(self, ids, embeddings=None, metadatas=None, documents=None): + self.add(ids, embeddings, metadatas, documents) + + def delete(self, ids=None, where=None, where_document=None): + query = "DELETE FROM memories WHERE category=%s AND id=ANY(%s)" + self.client.cur.execute(query, (self.category, ids)) + self.client.conn.commit() + +class PostgresCategory: + def __init__(self, name): + self.name = name + +class PostgresClient: + def __init__(self, connection_string): + self.conn = psycopg2.connect(connection_string) + self.cur = self.conn.cursor() + from pgvector.psycopg2 import register_vector + + register_vector(self.cur) # Register PGVector functions + self.model = SentenceTransformer("BAAI/bge-large-en") + self._create_memories_table() + + def list_collections(self): + self.cur.execute("SELECT DISTINCT category FROM memories") + # return a collection object with the key 'name' with the value category for each row (collection.name) + return [PostgresCategory(row) for row in self.cur.fetchall()] + + def delete_collection(self, category): + self.cur.execute("DELETE FROM memories WHERE category=%s", (category,)) + self.conn.commit() + + def _create_memories_table(self): + self.cur.execute( + """ + CREATE TABLE IF NOT EXISTS memories ( + id SERIAL PRIMARY KEY, + category TEXT NOT NULL, + document TEXT NOT NULL, + metadata JSONB, + embedding VECTOR(1024) + ) + """ + ) + self.conn.commit() + + def get_or_create_collection(self, category): + return PostgresCollection(category, self) + + def insert_memory(self, category, document, metadata={}, embedding=None): + metadata_string = json.dumps(metadata) # Convert the dict to a JSON string + query = """ + INSERT INTO memories (category, document, metadata, embedding) VALUES (%s, %s, %s, %s) + RETURNING id; + """ + self.cur.execute(query, (category, document, metadata_string, embedding)) + self.conn.commit() + return self.cur.fetchone()[0] + + def create_embedding(self, document): + return self.model.encode(document) + + def add(self, category, documents, metadatas, ids): + with self.connection.cursor() as cur: + for doc, meta, id_ in zip(documents, metadatas, ids): + embedding = self.create_embedding(doc) + cur.execute( + f""" + INSERT INTO {category} (id, document, metadata, embedding) + VALUES (%s, %s, %s, %s) + """, + (id_, doc, meta, embedding), + ) + self.connection.commit() + + def query(self, category, query_texts, n_results=5): + embeddings = [self.create_embedding(q) for q in query_texts] + results = [] + with self.connection.cursor() as cur: + for emb in embeddings: + cur.execute( + f""" + SELECT id, document, metadata, embedding <-> %s AS distance + FROM {category} + WHERE embedding <@- %s + ORDER BY embedding <-> %s + LIMIT %s + """, + (emb, emb, emb, n_results), + ) + results.extend(cur.fetchall()) + return results + + def update(self, category, id_, document=None, metadata=None): + with self.connection.cursor() as cur: + if document: + embedding = self.create_embedding(document) + cur.execute( + f""" + UPDATE {category} + SET document=%s, embedding=%s, metadata=%s + WHERE id=%s + """, + (document, embedding, metadata, id_), + ) + else: + cur.execute( + f""" + UPDATE {category} + SET metadata=%s + WHERE id=%s + """, + (metadata, id_), + ) + self.connection.commit() + + def close(self): + self.cur.close() + self.conn.close() diff --git a/agentmemory/helpers.py b/agentmemory/helpers.py index dee475d..0e7b30c 100644 --- a/agentmemory/helpers.py +++ b/agentmemory/helpers.py @@ -50,24 +50,29 @@ def chroma_collection_to_list(collection): collection (dict): Dictionary to be converted. Returns: - list: Converted list of dictionaries. + dict_list: Converted list of dictionaries. Example: >>> chroma_collection_to_list(collection) [{'metadata': '...', 'document': '...', 'id': '...'}] """ - list = [] + dict_list = [] + + # check if collection is a list + if isinstance(collection, list): + return collection + # If there are no embeddings, zip metadatas, documents and ids together if collection.get("embeddings", None) is None: for metadata, document, id in zip( collection["metadatas"], collection["documents"], collection["ids"] ): # append the zipped data as dictionary to the list - list.append({"metadata": metadata, "document": document, "id": id}) + dict_list.append({"metadata": metadata, "document": document, "id": id}) - return list + return dict_list # if distance is none, zip metadatas, documents, ids and embeddings together if collection.get("distances", None) is None: @@ -78,7 +83,7 @@ def chroma_collection_to_list(collection): collection["embeddings"], ): # append the zipped data as dictionary to the list - list.append( + dict_list.append( { "metadata": metadata, "document": document, @@ -87,7 +92,7 @@ def chroma_collection_to_list(collection): } ) - return list + return dict_list # if embeddings are present, zip all data including embeddings and distances for metadata, document, id, embedding, distance in zip( @@ -98,7 +103,7 @@ def chroma_collection_to_list(collection): collection.get("distances"), ): # append the zipped data as dictionary to the list - list.append( + dict_list.append( { "metadata": metadata, "document": document, @@ -107,8 +112,8 @@ def chroma_collection_to_list(collection): "id": id, } ) - debug_log("Collection to list", {"collection": collection, "list": list}) - return list + debug_log("Collection to list", {"collection": collection, "list": dict_list}) + return dict_list def list_to_chroma_collection(list): diff --git a/agentmemory/main.py b/agentmemory/main.py index 54e6401..b97eac3 100644 --- a/agentmemory/main.py +++ b/agentmemory/main.py @@ -8,7 +8,7 @@ ) -from agentmemory.client import get_chroma_client, check_client_initialized +from agentmemory.client import get_client def create_memory(category, text, metadata={}, embedding=None, id=None): @@ -28,10 +28,8 @@ def create_memory(category, text, metadata={}, embedding=None, id=None): >>> create_memory('sample_category', 'sample_text', id='sample_id', metadata={'sample_key': 'sample_value'}) """ - check_client_initialized() # client is lazy loaded, so make sure it is is initialized - # get or create the collection - memories = get_chroma_client().get_or_create_collection(category) + memories = get_client().get_or_create_collection(category) # add timestamps to metadata metadata["created_at"] = datetime.datetime.now().timestamp() @@ -136,14 +134,12 @@ def search_memory( [{'metadata': '...', 'document': '...', 'id': '...'}, {'metadata': '...', 'document': '...', 'id': '...'}] """ - check_client_initialized() # client is lazy loaded, so make sure it is is initialized - # check if contains_text is provided and format it for the query if contains_text is not None: contains_text = {"$contains": contains_text} # get or create the collection - memories = get_chroma_client().get_or_create_collection(category) + memories = get_client().get_or_create_collection(category) if (memories.count()) == 0: return [] @@ -209,10 +205,9 @@ def get_memory(category, id, include_embeddings=True): Example: >>> get_memory("books", "1") """ - check_client_initialized() # client is lazy loaded, so make sure it is is initialized # Get or create the collection for the given category - memories = get_chroma_client().get_or_create_collection(category) + memories = get_client().get_or_create_collection(category) # Get the types to include based on the function parameters include_types = get_include_types(include_embeddings, False) @@ -220,8 +215,10 @@ def get_memory(category, id, include_embeddings=True): # Retrieve the memory with the given ID memory = memories.get(ids=[str(id)], limit=1, include=include_types) - # Convert the collection to list format - memory = chroma_collection_to_list(memory) + # if memory is not a list + if not isinstance(memory, list): + # Convert the collection to list format + memory = chroma_collection_to_list(memory) debug_log(f"Got memory {id} from category {category}", memory) @@ -262,10 +259,9 @@ def get_memories( Example: >>> get_memories("books", sort_order="asc", n_results=10) """ - check_client_initialized() # client is lazy loaded, so make sure it is is initialized # Get or create the collection for the given category - memories = get_chroma_client().get_or_create_collection(category) + memories = get_client().get_or_create_collection(category) # min n_results to prevent searching for more elements than are available n_results = min(n_results, memories.count()) @@ -297,8 +293,12 @@ def get_memories( where=filter_metadata, where_document=where_document, include=include_types ) - # Convert the collection to list format - memories = chroma_collection_to_list(memories) + if not isinstance(memories, list): + # Convert the collection to list format + memories = chroma_collection_to_list(memories) + + print('memories') + print(memories) # Sort memories by ID. If sort_order is 'desc', then the reverse parameter will be True, and memories will be sorted in descending order. memories.sort(key=lambda x: x["id"], reverse=sort_order == "desc") @@ -331,10 +331,8 @@ def update_memory(category, id, text=None, metadata=None): >>> update_memory("books", "1", text="New text", metadata={"author": "New author"}) """ - check_client_initialized() # client is lazy loaded, so make sure it is is initialized - # Get or create the collection for the given category - memories = get_chroma_client().get_or_create_collection(category) + memories = get_client().get_or_create_collection(category) # If neither text nor metadata is provided, raise an exception if metadata is None and text is None: @@ -375,10 +373,8 @@ def delete_memory(category, id): >>> delete_memory("books", "1") """ - check_client_initialized() # client is lazy loaded, so make sure it is is initialized - # Get or create the collection for the given category - memories = get_chroma_client().get_or_create_collection(category) + memories = get_client().get_or_create_collection(category) if memory_exists(category, id) is False: debug_log( @@ -407,10 +403,9 @@ def delete_memories(category, document=None, metadata=None): Example: >>> delete_memories("books", document="Foundation", metadata={"author": "Isaac Asimov"}) """ - check_client_initialized() # client is lazy loaded, so make sure it is is initialized # Get or create the collection for the given category - memories = get_chroma_client().get_or_create_collection(category) + memories = get_client().get_or_create_collection(category) # Create a query to match either the document or the metadata if document is not None: @@ -477,10 +472,8 @@ def memory_exists(category, id, includes_metadata=None): >>> memory_exists("books", "1") """ - check_client_initialized() # client is lazy loaded, so make sure it is is initialized - # Get or create the collection for the given category - memories = get_chroma_client().get_or_create_collection(category) + memories = get_client().get_or_create_collection(category) # Check if there's a memory with the given ID and metadata memory = memories.get(ids=[str(id)], where=includes_metadata, limit=1) @@ -509,10 +502,8 @@ def count_memories(category, unique=False): >>> count_memories("books") """ - check_client_initialized() # client is lazy loaded, so make sure it is is initialized - # Get or create the collection for the given category - memories = get_chroma_client().get_or_create_collection(category) + memories = get_client().get_or_create_collection(category) if unique: memories = memories.get(where={"unique": "True"}) @@ -537,7 +528,7 @@ def wipe_category(category): collection = None try: - collection = get_chroma_client().get_collection( + collection = get_client().get_collection( category ) # Check if the category exists except Exception: @@ -548,7 +539,7 @@ def wipe_category(category): if collection is not None: # Delete the entire category - get_chroma_client().delete_collection(category) + get_client().delete_collection(category) def wipe_all_memories(): @@ -558,9 +549,7 @@ def wipe_all_memories(): Example: >>> wipe_all_memories() """ - - check_client_initialized() # client is lazy loaded, so make sure it is is initialized - client = get_chroma_client() + client = get_client() collections = client.list_collections() # Iterate over all collections diff --git a/agentmemory/main_no_comments.py b/agentmemory/main_no_comments.py new file mode 100644 index 0000000..f15990d --- /dev/null +++ b/agentmemory/main_no_comments.py @@ -0,0 +1,212 @@ +import datetime + +from agentmemory.helpers import ( + chroma_collection_to_list, + flatten_arrays, + get_include_types, +) + +from agentmemory.client import get_client + +def create_memory(category, text, metadata={}, embedding=None, id=None): + memories = get_client().get_or_create_collection(category) + metadata["created_at"] = datetime.datetime.now().timestamp() + metadata["updated_at"] = datetime.datetime.now().timestamp() + if id is None: + id = str(memories.count()) + id = id.zfill(16) + for key, value in metadata.items(): + if isinstance(value, bool): + metadata[key] = str(value) + memories.upsert( + ids=[str(id)], + documents=[text], + metadatas=[metadata], + embeddings=[embedding] if embedding is not None else None, + ) + return id + + +def create_unique_memory(category, content, metadata={}, similarity=0.95): + max_distance = 1.0 - similarity + memories = search_memory( + category, + min_distance=0, + max_distance=max_distance, + search_text=content, + n_results=1, + filter_metadata={"unique": "True"}, + ) + if len(memories) == 0: + metadata["unique"] = "True" + create_memory(category, content, metadata=metadata) + return + metadata["unique"] = "False" + metadata["related_to"] = memories[0]["id"] + metadata["related_document"] = memories[0]["document"] + create_memory(category, content, metadata=metadata) + return id + +def search_memory( + category, + search_text, + n_results=5, + filter_metadata=None, + contains_text=None, + include_embeddings=True, + include_distances=True, + max_distance=None, # 0.0 - 1.0 + min_distance=None, # 0.0 - 1.0 + unique=False, +): + if contains_text is not None: + contains_text = {"$contains": contains_text} + memories = get_client().get_or_create_collection(category) + if (memories.count()) == 0: + return [] + n_results = min(n_results, memories.count()) + include_types = get_include_types(include_embeddings, include_distances) + if filter_metadata is not None and len(filter_metadata.keys()) > 1: + # map each key:value in filter_metadata to an object shaped like { "key": { "$eq": "value" } } + filter_metadata = [ + {key: {"$eq": value}} for key, value in filter_metadata.items() + ] + filter_metadata = {"$and": filter_metadata} + if unique: + if filter_metadata is None: + filter_metadata = {} + filter_metadata["unique"] = "True" + query = memories.query( + query_texts=[search_text], + where=filter_metadata, + where_document=contains_text, + n_results=n_results, + include=include_types, + ) + query = flatten_arrays(query) + result_list = chroma_collection_to_list(query) + if min_distance is not None and min_distance > 0: + result_list = [res for res in result_list if res["distance"] >= min_distance] + if max_distance is not None and max_distance < 1.0: + result_list = [res for res in result_list if res["distance"] <= max_distance] + return result_list + + +def get_memory(category, id, include_embeddings=True): + memories = get_client().get_or_create_collection(category) + include_types = get_include_types(include_embeddings, False) + memory = memories.get(ids=[str(id)], limit=1, include=include_types) + if not isinstance(memory, list): + memory = chroma_collection_to_list(memory) + if len(memory) == 0: + return None + return memory[0] + +def get_memories( + category, + sort_order="desc", + contains_text=None, + filter_metadata=None, + n_results=20, + include_embeddings=True, + unique=False, +): + memories = get_client().get_or_create_collection(category) + n_results = min(n_results, memories.count()) + include_types = get_include_types(include_embeddings, False) + where_document = None + if contains_text is not None: + where_document = {"$contains": contains_text} + if filter_metadata is not None and len(filter_metadata.keys()) > 1: + filter_metadata = [ + {key: {"$eq": value}} for key, value in filter_metadata.items() + ] + filter_metadata = {"$and": filter_metadata} + if unique: + if filter_metadata is None: + filter_metadata = {} + filter_metadata["unique"] = "True" + memories = memories.get( + where=filter_metadata, where_document=where_document, include=include_types + ) + if not isinstance(memories, list): + memories = chroma_collection_to_list(memories) + memories.sort(key=lambda x: x["id"], reverse=sort_order == "desc") + memories = memories[:n_results] + return memories + + +def update_memory(category, id, text=None, metadata=None): + memories = get_client().get_or_create_collection(category) + if metadata is None and text is None: + raise Exception("No text or metadata provided") + if metadata is not None: + for key, value in metadata.items(): + if isinstance(value, bool): + metadata[key] = str(value) + metadata["updated_at"] = datetime.datetime.now().timestamp() + documents = [text] if text is not None else None + metadatas = [metadata] if metadata is not None else None + memories.update(ids=[str(id)], documents=documents, metadatas=metadatas) + + +def delete_memory(category, id): + memories = get_client().get_or_create_collection(category) + if memory_exists(category, id) is False: + return + memories.delete(ids=[str(id)]) + +def delete_memories(category, document=None, metadata=None): + memories = get_client().get_or_create_collection(category) + if document is not None: + memories.delete(where_document={"$contains": document}) + if metadata is not None: + memories.delete(where=metadata) + return True + + +def delete_similar_memories(category, content, similarity_threshold=0.95): + memories = search_memory(category, content) + memories_to_delete = [] + if len(memories) > 0: + for memory in memories: + goal_similarity = 1.0 - memory["distance"] + if goal_similarity > similarity_threshold: + memories_to_delete.append(memory["id"]) + else: + break + if len(memories_to_delete) > 0: + for memory in memories_to_delete: + delete_memory(category, memory) + return len(memories_to_delete) > 0 + + +def memory_exists(category, id, includes_metadata=None): + memories = get_client().get_or_create_collection(category) + memory = memories.get(ids=[str(id)], where=includes_metadata, limit=1) + exists = len(memory["ids"]) > 0 + return exists + +def count_memories(category, unique=False): + memories = get_client().get_or_create_collection(category) + if unique: + memories = memories.get(where={"unique": "True"}) + return memories.count() + + +def wipe_category(category): + collection = None + try: + collection = get_client().get_collection(category) + except Exception: + pass + + if collection is not None: + get_client().delete_collection(category) + + +def wipe_all_memories(): + client = get_client() + collections = client.list_collections() + for collection in collections: + client.delete_collection(collection.name) \ No newline at end of file diff --git a/agentmemory/persistence.py b/agentmemory/persistence.py index 0155d37..76ede30 100644 --- a/agentmemory/persistence.py +++ b/agentmemory/persistence.py @@ -4,7 +4,7 @@ get_memories, wipe_all_memories, ) -from agentmemory.client import get_chroma_client +from agentmemory.client import get_client def export_memory_to_json(include_embeddings=True): @@ -22,7 +22,7 @@ def export_memory_to_json(include_embeddings=True): >>> export_memory_to_json() """ - collections = get_chroma_client().list_collections() + collections = get_client().list_collections() collections_dict = {} diff --git a/agentmemory/tests/helpers.py b/agentmemory/tests/helpers.py index c4d6002..16090fd 100644 --- a/agentmemory/tests/helpers.py +++ b/agentmemory/tests/helpers.py @@ -5,7 +5,7 @@ from agentmemory import ( chroma_collection_to_list, create_memory, - get_chroma_client, + get_client, list_to_chroma_collection, wipe_all_memories, ) @@ -17,16 +17,12 @@ import_json_to_memory, ) -from agentmemory.client import set_chroma_client - def test_chroma_collection_conversion(): - # chroma_collection_to_list - set_chroma_client("./memory") wipe_all_memories() create_memory("test", "document 1", metadata={"test": "test"}) create_memory("test", "document 2", metadata={"test": "test"}) - collection = get_chroma_client().get_or_create_collection("test") + collection = get_client().get_or_create_collection("test") test_collection_data = collection.peek() list_data = chroma_collection_to_list(test_collection_data) @@ -52,9 +48,8 @@ def test_chroma_collection_conversion(): def test_get_chroma_client(): - set_chroma_client("./memory") wipe_all_memories() - client = get_chroma_client() + client = get_client() assert client is not None, "Client should not be None" @@ -141,14 +136,14 @@ def test_export_memory_to_file(): def test_import_json_to_memory(): # Test with empty dictionary import_json_to_memory(data={}) - collections = get_chroma_client().list_collections() + collections = get_client().list_collections() assert len(collections) == 0, "No collections should exist" # Test with replace=False create_memory("test", "document 1") data = export_memory_to_json() import_json_to_memory(data, replace=False) - collections = get_chroma_client().list_collections() + collections = get_client().list_collections() assert len(collections) == 1, "Only one collection should exist" # Test with invalid data @@ -161,12 +156,12 @@ def test_import_file_to_memory(): # Test with default parameters export_memory_to_file() import_file_to_memory() - assert get_chroma_client().list_collections(), "Collections should exist" + assert get_client().list_collections(), "Collections should exist" # Test with specified path export_memory_to_file(path="./test_memory.json") import_file_to_memory(path="./test_memory.json") - assert get_chroma_client().list_collections(), "Collections should exist" + assert get_client().list_collections(), "Collections should exist" # Test with invalid path with pytest.raises(Exception): diff --git a/agentmemory/tests/main.py b/agentmemory/tests/main.py index b69aaba..f41e20e 100644 --- a/agentmemory/tests/main.py +++ b/agentmemory/tests/main.py @@ -28,6 +28,9 @@ def test_memory_creation_and_retrieval(): # assert length of memories is 10 assert len(memories) == 10 + print('******** memories[0]') + print(memories[0]) + # assert that the first memory is document 19 assert memories[0]["document"] == "document 19" wipe_category("test") diff --git a/agentmemory/tests/persistence.py b/agentmemory/tests/persistence.py index 6c2379c..d194b59 100644 --- a/agentmemory/tests/persistence.py +++ b/agentmemory/tests/persistence.py @@ -9,11 +9,9 @@ import_json_to_memory, wipe_all_memories, ) -from agentmemory.client import set_chroma_client def test_memory_export_import(): - set_chroma_client("./memory") wipe_all_memories() create_memory("test", "not document 1", metadata={"test": "test"}) export_memory_to_file("./test_memories.json") @@ -25,7 +23,6 @@ def test_memory_export_import(): def test_export_memory_to_json(): - set_chroma_client("./memory") create_memory("test", "document 1", metadata={"test": "test"}) export_dict = export_memory_to_json() assert "test" in export_dict @@ -33,7 +30,6 @@ def test_export_memory_to_json(): def test_import_json_to_memory(): - set_chroma_client("./memory") data = { "test": [{"document": "document 1", "metadata": {"test": "test"}, "id": "1"}] } @@ -43,7 +39,6 @@ def test_import_json_to_memory(): def test_import_file_to_memory(): - set_chroma_client("./memory") create_memory("test", "document 1", metadata={"test": "test"}) export_memory_to_file("./test_memories.json") # Wipe out all memories diff --git a/requirements.txt b/requirements.txt index 2cfc6eb..b63ca2b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,2 +1,5 @@ chromadb -agentlogger \ No newline at end of file +agentlogger +psycopg2-binary +sentence_transformers +python-dotenv \ No newline at end of file From 4ec5388dfa058742a73ba10e3019da8d59c20d6c Mon Sep 17 00:00:00 2001 From: moon Date: Tue, 8 Aug 2023 19:38:04 -0700 Subject: [PATCH 2/5] 6/11 tests passing for postgres client --- agentmemory/client.py | 114 ++++++++++++++++++++++++++------------ agentmemory/main.py | 12 ++-- agentmemory/tests/main.py | 8 ++- 3 files changed, 88 insertions(+), 46 deletions(-) diff --git a/agentmemory/client.py b/agentmemory/client.py index eb67a74..677ea0a 100644 --- a/agentmemory/client.py +++ b/agentmemory/client.py @@ -19,6 +19,9 @@ def get_client(client_type=None, *args, **kwargs): global client + if client is not None: + return client + if client_type is None: client_type = CLIENT_TYPE @@ -27,13 +30,11 @@ def get_client(client_type=None, *args, **kwargs): raise EnvironmentError( "Postgres connection string not set in environment variables!" ) - if client is None: - client = PostgresClient(POSTGRES_CONNECTION_STRING) - return client + client = PostgresClient(POSTGRES_CONNECTION_STRING) else: - if client is None: - client = chromadb.PersistentClient(path=STORAGE_PATH, *args, **kwargs) - return client + client = chromadb.PersistentClient(path=STORAGE_PATH, *args, **kwargs) + + return client class PostgresCollection: @@ -64,13 +65,32 @@ def get( where_document=None, include=["metadatas", "documents"], ): - query = f"SELECT * FROM memories WHERE category=%s LIMIT %s OFFSET %s" - self.client.cur.execute(query, (self.category, limit, offset)) + if ids: + if not all(isinstance(i, str) or isinstance(i, int) for i in ids): + raise Exception("ids must be a list of integers or strings representing integers") + + # Map ids to integers + ids_array = ",".join(map(str, [int(i) for i in ids])) + + # Adjusting the query to cast the entire ARRAY after it's constructed + query = f"SELECT * FROM memories WHERE category=%s AND id=ANY(ARRAY[{ids_array}]::integer[]) LIMIT %s OFFSET %s" + self.client.cur.execute(query, (self.category, limit, offset)) + + else: + query = f"SELECT * FROM memories WHERE category=%s LIMIT %s OFFSET %s" + self.client.cur.execute(query, (self.category, limit, offset)) rows = self.client.cur.fetchall() # Convert rows to list of dictionaries columns = [desc[0] for desc in self.client.cur.description] - return [dict(zip(columns, row)) for row in rows] + result = [dict(zip(columns, row)) for row in rows] + + return { + "ids": [row["id"] for row in result], + "documents": [row["document"] for row in result], + "metadatas": [row["metadata"] for row in result], + } + def peek(self, limit=10): @@ -87,26 +107,34 @@ def query( ): return self.client.query(self.category, query_texts, n_results) - def update(self, ids, embeddings=None, metadatas=None, documents=None): - for id_, doc, meta, emb in zip(ids, documents, metadatas, embeddings): - self.client.update(self.category, id_, doc, meta) + def update(self, ids, documents=None, metadatas=None, embeddings=None): + # if embeddings is not None + if embeddings is None: + for id_, doc, meta in zip(ids, documents, metadatas): + self.client.update(self.category, id_, doc, meta) + else: + for id_, doc, meta, emb in zip(ids, documents, metadatas, embeddings): + self.client.update(self.category, id_, doc, meta, emb) def upsert(self, ids, embeddings=None, metadatas=None, documents=None): self.add(ids, embeddings, metadatas, documents) def delete(self, ids=None, where=None, where_document=None): - query = "DELETE FROM memories WHERE category=%s AND id=ANY(%s)" + ids = [int(i) for i in ids] + query = "DELETE FROM memories WHERE category=%s AND id=ANY(%s::int[])" self.client.cur.execute(query, (self.category, ids)) - self.client.conn.commit() + self.client.connection.commit() + class PostgresCategory: def __init__(self, name): self.name = name + class PostgresClient: def __init__(self, connection_string): - self.conn = psycopg2.connect(connection_string) - self.cur = self.conn.cursor() + self.connection = psycopg2.connect(connection_string) + self.cur = self.connection.cursor() from pgvector.psycopg2 import register_vector register_vector(self.cur) # Register PGVector functions @@ -115,13 +143,16 @@ def __init__(self, connection_string): def list_collections(self): self.cur.execute("SELECT DISTINCT category FROM memories") - # return a collection object with the key 'name' with the value category for each row (collection.name) return [PostgresCategory(row) for row in self.cur.fetchall()] - + + def get_collection(self, category): + return PostgresCollection(category, self) + def delete_collection(self, category): + print("deleting collection", category) self.cur.execute("DELETE FROM memories WHERE category=%s", (category,)) - self.conn.commit() - + self.connection.commit() + def _create_memories_table(self): self.cur.execute( """ @@ -134,19 +165,22 @@ def _create_memories_table(self): ) """ ) - self.conn.commit() + self.connection.commit() def get_or_create_collection(self, category): return PostgresCollection(category, self) def insert_memory(self, category, document, metadata={}, embedding=None): metadata_string = json.dumps(metadata) # Convert the dict to a JSON string + if embedding is None: + embedding = self.create_embedding(document) + query = """ INSERT INTO memories (category, document, metadata, embedding) VALUES (%s, %s, %s, %s) RETURNING id; """ self.cur.execute(query, (category, document, metadata_string, embedding)) - self.conn.commit() + self.connection.commit() return self.cur.fetchone()[0] def create_embedding(self, document): @@ -157,11 +191,11 @@ def add(self, category, documents, metadatas, ids): for doc, meta, id_ in zip(documents, metadatas, ids): embedding = self.create_embedding(doc) cur.execute( - f""" - INSERT INTO {category} (id, document, metadata, embedding) - VALUES (%s, %s, %s, %s) + """ + INSERT INTO memories (id, category, document, metadata, embedding) + VALUES (%s, %s %s, %s, %s) """, - (id_, doc, meta, embedding), + (id_, category, doc, meta, embedding), ) self.connection.commit() @@ -173,34 +207,42 @@ def query(self, category, query_texts, n_results=5): cur.execute( f""" SELECT id, document, metadata, embedding <-> %s AS distance - FROM {category} - WHERE embedding <@- %s + FROM memories + WHERE category = %s ORDER BY embedding <-> %s LIMIT %s """, - (emb, emb, emb, n_results), + (emb, category, emb, n_results), ) results.extend(cur.fetchall()) return results - def update(self, category, id_, document=None, metadata=None): + def update(self, category, id_, document=None, metadata=None, embedding=None): with self.connection.cursor() as cur: if document: - embedding = self.create_embedding(document) + print("updating document") + print(document) + print(metadata) + # if metadata is a dict, convert it to a JSON string + if isinstance(metadata, dict): + metadata = json.dumps(metadata) + print(id_) + if embedding is None: + embedding = self.create_embedding(document) cur.execute( f""" - UPDATE {category} + UPDATE memories SET document=%s, embedding=%s, metadata=%s - WHERE id=%s + WHERE id=%s AND category='{category}' """, (document, embedding, metadata, id_), ) else: cur.execute( f""" - UPDATE {category} + UPDATE memories SET metadata=%s - WHERE id=%s + WHERE id=%s AND category='{category}' """, (metadata, id_), ) @@ -208,4 +250,4 @@ def update(self, category, id_, document=None, metadata=None): def close(self): self.cur.close() - self.conn.close() + self.connection.close() diff --git a/agentmemory/main.py b/agentmemory/main.py index b97eac3..b1c482a 100644 --- a/agentmemory/main.py +++ b/agentmemory/main.py @@ -215,10 +215,8 @@ def get_memory(category, id, include_embeddings=True): # Retrieve the memory with the given ID memory = memories.get(ids=[str(id)], limit=1, include=include_types) - # if memory is not a list - if not isinstance(memory, list): - # Convert the collection to list format - memory = chroma_collection_to_list(memory) + + memory = chroma_collection_to_list(memory) debug_log(f"Got memory {id} from category {category}", memory) @@ -311,7 +309,7 @@ def get_memories( return memories -def update_memory(category, id, text=None, metadata=None): +def update_memory(category, id, text=None, metadata=None, embedding=None): """ Update a memory with new text and/or metadata. @@ -348,9 +346,10 @@ def update_memory(category, id, text=None, metadata=None): documents = [text] if text is not None else None metadatas = [metadata] if metadata is not None else None + embeddings = [embedding] if embedding is not None else None # Update the memory with the new text and/or metadata - memories.update(ids=[str(id)], documents=documents, metadatas=metadatas) + memories.update(ids=[str(id)], documents=documents, metadatas=metadatas, embeddings=embeddings) debug_log( f"Updated memory {id} in category {category}", @@ -541,7 +540,6 @@ def wipe_category(category): # Delete the entire category get_client().delete_collection(category) - def wipe_all_memories(): """ Delete all memories across all categories. diff --git a/agentmemory/tests/main.py b/agentmemory/tests/main.py index f41e20e..cdcc863 100644 --- a/agentmemory/tests/main.py +++ b/agentmemory/tests/main.py @@ -28,9 +28,6 @@ def test_memory_creation_and_retrieval(): # assert length of memories is 10 assert len(memories) == 10 - print('******** memories[0]') - print(memories[0]) - # assert that the first memory is document 19 assert memories[0]["document"] == "document 19" wipe_category("test") @@ -41,6 +38,7 @@ def test_memory_deletion(): # Delete memory test create_memory("test", "delete memory test", metadata={"test": "test"}) memories = get_memories("test") + print('memories', memories) memory_id = memories[0]["id"] num_memories = count_memories("test") # test delete_memory @@ -85,6 +83,10 @@ def test_search_memory(): def test_wipe_category(): # test wipe_category wipe_category("test") + for i in range(3): + create_memory("test", "document " + str(i + 1), metadata={"test": "test"}) + assert count_memories("test") == 3 + wipe_category("test") assert count_memories("test") == 0 From e24a1b8eb2e946fd3c2fc27233b7865b4d43f925 Mon Sep 17 00:00:00 2001 From: moon Date: Tue, 8 Aug 2023 22:25:18 -0700 Subject: [PATCH 3/5] 7/11 tests passing --- agentmemory/client.py | 220 +----------------------- agentmemory/main.py | 16 +- agentmemory/postgres.py | 315 ++++++++++++++++++++++++++++++++++ agentmemory/tests/__init__.py | 8 +- agentmemory/tests/main.py | 39 +++-- 5 files changed, 353 insertions(+), 245 deletions(-) create mode 100644 agentmemory/postgres.py diff --git a/agentmemory/client.py b/agentmemory/client.py index 677ea0a..8b77347 100644 --- a/agentmemory/client.py +++ b/agentmemory/client.py @@ -6,6 +6,8 @@ from sentence_transformers import SentenceTransformer from dotenv import load_dotenv +from agentmemory.postgres import PostgresClient + load_dotenv() @@ -34,220 +36,4 @@ def get_client(client_type=None, *args, **kwargs): else: client = chromadb.PersistentClient(path=STORAGE_PATH, *args, **kwargs) - return client - - -class PostgresCollection: - def __init__(self, category, client): - self.category = category - self.client = client - - def count(self): - self.client.cur.execute( - f"SELECT COUNT(*) FROM memories WHERE category=%s", (self.category,) - ) - return self.client.cur.fetchone()[0] - - def add(self, ids, embeddings=None, metadatas=None, documents=None): - if embeddings is None: - for id_, doc, meta in zip(ids, documents, metadatas): - self.client.insert_memory(self.category, doc, meta) - else: - for id_, doc, meta, emb in zip(ids, documents, metadatas, embeddings): - self.client.insert_memory(self.category, doc, meta, emb) - - def get( - self, - ids=None, - where=None, - limit=None, - offset=None, - where_document=None, - include=["metadatas", "documents"], - ): - if ids: - if not all(isinstance(i, str) or isinstance(i, int) for i in ids): - raise Exception("ids must be a list of integers or strings representing integers") - - # Map ids to integers - ids_array = ",".join(map(str, [int(i) for i in ids])) - - # Adjusting the query to cast the entire ARRAY after it's constructed - query = f"SELECT * FROM memories WHERE category=%s AND id=ANY(ARRAY[{ids_array}]::integer[]) LIMIT %s OFFSET %s" - self.client.cur.execute(query, (self.category, limit, offset)) - - else: - query = f"SELECT * FROM memories WHERE category=%s LIMIT %s OFFSET %s" - self.client.cur.execute(query, (self.category, limit, offset)) - rows = self.client.cur.fetchall() - - # Convert rows to list of dictionaries - columns = [desc[0] for desc in self.client.cur.description] - result = [dict(zip(columns, row)) for row in rows] - - return { - "ids": [row["id"] for row in result], - "documents": [row["document"] for row in result], - "metadatas": [row["metadata"] for row in result], - } - - - - def peek(self, limit=10): - return self.get(limit=limit) - - def query( - self, - query_embeddings=None, - query_texts=None, - n_results=10, - where=None, - where_document=None, - include=["metadatas", "documents", "distances"], - ): - return self.client.query(self.category, query_texts, n_results) - - def update(self, ids, documents=None, metadatas=None, embeddings=None): - # if embeddings is not None - if embeddings is None: - for id_, doc, meta in zip(ids, documents, metadatas): - self.client.update(self.category, id_, doc, meta) - else: - for id_, doc, meta, emb in zip(ids, documents, metadatas, embeddings): - self.client.update(self.category, id_, doc, meta, emb) - - def upsert(self, ids, embeddings=None, metadatas=None, documents=None): - self.add(ids, embeddings, metadatas, documents) - - def delete(self, ids=None, where=None, where_document=None): - ids = [int(i) for i in ids] - query = "DELETE FROM memories WHERE category=%s AND id=ANY(%s::int[])" - self.client.cur.execute(query, (self.category, ids)) - self.client.connection.commit() - - -class PostgresCategory: - def __init__(self, name): - self.name = name - - -class PostgresClient: - def __init__(self, connection_string): - self.connection = psycopg2.connect(connection_string) - self.cur = self.connection.cursor() - from pgvector.psycopg2 import register_vector - - register_vector(self.cur) # Register PGVector functions - self.model = SentenceTransformer("BAAI/bge-large-en") - self._create_memories_table() - - def list_collections(self): - self.cur.execute("SELECT DISTINCT category FROM memories") - return [PostgresCategory(row) for row in self.cur.fetchall()] - - def get_collection(self, category): - return PostgresCollection(category, self) - - def delete_collection(self, category): - print("deleting collection", category) - self.cur.execute("DELETE FROM memories WHERE category=%s", (category,)) - self.connection.commit() - - def _create_memories_table(self): - self.cur.execute( - """ - CREATE TABLE IF NOT EXISTS memories ( - id SERIAL PRIMARY KEY, - category TEXT NOT NULL, - document TEXT NOT NULL, - metadata JSONB, - embedding VECTOR(1024) - ) - """ - ) - self.connection.commit() - - def get_or_create_collection(self, category): - return PostgresCollection(category, self) - - def insert_memory(self, category, document, metadata={}, embedding=None): - metadata_string = json.dumps(metadata) # Convert the dict to a JSON string - if embedding is None: - embedding = self.create_embedding(document) - - query = """ - INSERT INTO memories (category, document, metadata, embedding) VALUES (%s, %s, %s, %s) - RETURNING id; - """ - self.cur.execute(query, (category, document, metadata_string, embedding)) - self.connection.commit() - return self.cur.fetchone()[0] - - def create_embedding(self, document): - return self.model.encode(document) - - def add(self, category, documents, metadatas, ids): - with self.connection.cursor() as cur: - for doc, meta, id_ in zip(documents, metadatas, ids): - embedding = self.create_embedding(doc) - cur.execute( - """ - INSERT INTO memories (id, category, document, metadata, embedding) - VALUES (%s, %s %s, %s, %s) - """, - (id_, category, doc, meta, embedding), - ) - self.connection.commit() - - def query(self, category, query_texts, n_results=5): - embeddings = [self.create_embedding(q) for q in query_texts] - results = [] - with self.connection.cursor() as cur: - for emb in embeddings: - cur.execute( - f""" - SELECT id, document, metadata, embedding <-> %s AS distance - FROM memories - WHERE category = %s - ORDER BY embedding <-> %s - LIMIT %s - """, - (emb, category, emb, n_results), - ) - results.extend(cur.fetchall()) - return results - - def update(self, category, id_, document=None, metadata=None, embedding=None): - with self.connection.cursor() as cur: - if document: - print("updating document") - print(document) - print(metadata) - # if metadata is a dict, convert it to a JSON string - if isinstance(metadata, dict): - metadata = json.dumps(metadata) - print(id_) - if embedding is None: - embedding = self.create_embedding(document) - cur.execute( - f""" - UPDATE memories - SET document=%s, embedding=%s, metadata=%s - WHERE id=%s AND category='{category}' - """, - (document, embedding, metadata, id_), - ) - else: - cur.execute( - f""" - UPDATE memories - SET metadata=%s - WHERE id=%s AND category='{category}' - """, - (metadata, id_), - ) - self.connection.commit() - - def close(self): - self.cur.close() - self.connection.close() + return client \ No newline at end of file diff --git a/agentmemory/main.py b/agentmemory/main.py index b1c482a..33d0918 100644 --- a/agentmemory/main.py +++ b/agentmemory/main.py @@ -212,10 +212,13 @@ def get_memory(category, id, include_embeddings=True): # Get the types to include based on the function parameters include_types = get_include_types(include_embeddings, False) + memory2 = memories.get(limit=1, include=include_types) + print("***** memory no ids returned:") + print(memory2) # Retrieve the memory with the given ID - memory = memories.get(ids=[str(id)], limit=1, include=include_types) - - + memory = memories.get(ids=[id], limit=1, include=include_types) + print("***** memory returned:") + print(memory) memory = chroma_collection_to_list(memory) debug_log(f"Got memory {id} from category {category}", memory) @@ -295,7 +298,7 @@ def get_memories( # Convert the collection to list format memories = chroma_collection_to_list(memories) - print('memories') + print("memories") print(memories) # Sort memories by ID. If sort_order is 'desc', then the reverse parameter will be True, and memories will be sorted in descending order. @@ -349,7 +352,9 @@ def update_memory(category, id, text=None, metadata=None, embedding=None): embeddings = [embedding] if embedding is not None else None # Update the memory with the new text and/or metadata - memories.update(ids=[str(id)], documents=documents, metadatas=metadatas, embeddings=embeddings) + memories.update( + ids=[str(id)], documents=documents, metadatas=metadatas, embeddings=embeddings + ) debug_log( f"Updated memory {id} in category {category}", @@ -540,6 +545,7 @@ def wipe_category(category): # Delete the entire category get_client().delete_collection(category) + def wipe_all_memories(): """ Delete all memories across all categories. diff --git a/agentmemory/postgres.py b/agentmemory/postgres.py new file mode 100644 index 0000000..d35074e --- /dev/null +++ b/agentmemory/postgres.py @@ -0,0 +1,315 @@ +import json +import psycopg2 +from sentence_transformers import SentenceTransformer + + +class PostgresCollection: + def __init__(self, category, client): + self.category = category + self.client = client + + def count(self): + self.client.ensure_table_exists(self.category) + table_name = self.client._table_name(self.category) + + query = f"SELECT COUNT(*) FROM {table_name}" + self.client.cur.execute(query) + + return self.client.cur.fetchone()[0] + + def add(self, ids, documents=None, metadatas=None, embeddings=None): + if embeddings is None: + for id_, document, metadata in zip(ids, documents, metadatas): + self.client.insert_memory(self.category, document, metadata) + else: + for id_, document, metadata, emb in zip( + ids, documents, metadatas, embeddings + ): + self.client.insert_memory(self.category, document, metadata, emb) + + def get( + self, + ids=None, + where=None, + limit=None, + offset=None, + where_document=None, + include=["metadatas", "documents"], + ): + category = self.category + table_name = self.client._table_name(category) + if not ids: + if limit is None: + limit = 100 # or another default value + if offset is None: + offset = 0 + + query = f"SELECT * FROM {table_name} LIMIT %s OFFSET %s" + params = (limit, offset) + + else: + if not all(isinstance(i, str) or isinstance(i, int) for i in ids): + raise Exception( + "ids must be a list of integers or strings representing integers" + ) + + if limit is None: + limit = len(ids) + if offset is None: + offset = 0 + + table_name = self.client._table_name(category) + ids = [int(i) for i in ids] + + query = f"SELECT * FROM {table_name} WHERE id=ANY(%s) LIMIT %s OFFSET %s" + params = (ids, limit, offset) + + self.client.cur.execute(query, params) + rows = self.client.cur.fetchall() + + # Convert rows to list of dictionaries + columns = [desc[0] for desc in self.client.cur.description] + result = [dict(zip(columns, row)) for row in rows] + + return { + "ids": [row["id"] for row in result], + "documents": [row["document"] for row in result], + "metadatas": [row["metadata"] for row in result], + } + + def peek(self, limit=10): + return self.get(limit=limit) + + def query( + self, + query_embeddings=None, + query_texts=None, + n_results=10, + where=None, + where_document=None, + include=["metadatas", "documents", "distances"], + ): + return self.client.query(self.category, query_texts, n_results) + + def update(self, ids, documents=None, metadatas=None, embeddings=None): + # if embeddings is not None + if embeddings is None: + for id_, document, metadata in zip(ids, documents, metadatas): + self.client.update(self.category, id_, document, metadata) + else: + for id_, document, metadata, emb in zip( + ids, documents, metadatas, embeddings + ): + self.client.update(self.category, id_, document, metadata, emb) + + def upsert(self, ids, documents=None, metadatas=None, embeddings=None): + self.add(ids, documents, metadatas, embeddings) + + def delete(self, ids=None, where=None, where_document=None): + table_name = self.client._table_name(self.category) + # check if table exists + self.client.ensure_table_exists(self.category) + + # Base of the query + query = f"DELETE FROM {table_name}" + params = [] + + conditions = [] + + if ids is not None: + if not all(isinstance(i, (int, str)) and str(i).isdigit() for i in ids): + raise Exception( + "ids must be a list of integers or strings representing integers" + ) + ids = [int(i) for i in ids] + conditions.append("id=ANY(%s::int[])") + params.append(ids) + + if where_document is not None: + if "$contains" in where_document: + conditions.append("document LIKE %s") + params.append(f"%{where_document['$contains']}%") + # You can add more operators for 'where_document' here if needed + + if where is not None: + for key, value in where.items(): + conditions.append(f"{key}=%s") + params.append(value) + + if conditions: + query += " WHERE " + " AND ".join(conditions) + else: + raise Exception("No valid conditions provided for deletion.") + + self.client.cur.execute(query, tuple(params)) + self.client.connection.commit() + + +class PostgresCategory: + def __init__(self, name): + self.name = name + + +class PostgresClient: + def __init__(self, connection_string): + self.connection = psycopg2.connect(connection_string) + self.cur = self.connection.cursor() + from pgvector.psycopg2 import register_vector + + register_vector(self.cur) # Register PGVector functions + self.model = SentenceTransformer("BAAI/bge-large-en") + + def _table_name(self, category): + return f"memory_{category}" + + def ensure_table_exists(self, category): + table_name = self._table_name(category) + self.cur.execute( + f""" + CREATE TABLE IF NOT EXISTS {table_name} ( + id SERIAL PRIMARY KEY, + document TEXT NOT NULL, + metadata JSONB, + embedding VECTOR(1024) + ) + """ + ) + self.connection.commit() + + def _ensure_metadata_columns_exist(self, category, metadata): + table_name = self._table_name(category) + for key in metadata.keys(): + self.cur.execute( + """ + SELECT EXISTS ( + SELECT 1 + FROM pg_catalog.pg_attribute + WHERE attrelid = %s::regclass + AND attname = %s + AND NOT attisdropped + ) + """, + (table_name, key), + ) + exists = self.cur.fetchone()[0] + if not exists: + self.cur.execute(f"ALTER TABLE {table_name} ADD COLUMN {key} TEXT") + self.connection.commit() + + def list_collections(self): + self.cur.execute( + "SELECT table_name FROM information_schema.tables WHERE table_schema='public'" + ) + return [ + PostgresCategory(row[0].split("_")[1]) + for row in self.cur.fetchall() + if row[0].startswith("memory_") + ] + + def get_collection(self, category): + return PostgresCollection(category, self) + + def delete_collection(self, category): + print("deleting collection", category) + table_name = self._table_name(category) + self.cur.execute(f"DROP TABLE IF EXISTS {table_name}") + self.connection.commit() + + def get_or_create_collection(self, category): + return PostgresCollection(category, self) + + def insert_memory(self, category, document, metadata={}, embedding=None, id=None): + self.ensure_table_exists(category) + self._ensure_metadata_columns_exist(category, metadata) + table_name = self._table_name(category) + + metadata_string = json.dumps(metadata) # Convert the dict to a JSON string + if embedding is None: + embedding = self.create_embedding(document) + + # if the id is None, get the length of the table by counting the number of rows in the category + if id is None: + id = self.get_or_create_collection(category).count() + + query = f""" + INSERT INTO {table_name} (id, document, metadata, embedding) VALUES (%s, %s, %s, %s) + RETURNING id; + """ + self.cur.execute(query, (id, document, metadata_string, embedding)) + self.connection.commit() + return self.cur.fetchone()[0] + + def create_embedding(self, document): + return self.model.encode(document) + + def add(self, category, documents, metadatas, ids): + self.ensure_table_exists(category) + table_name = self._table_name(category) + with self.connection.cursor() as cur: + for document, metadata, id_ in zip(documents, metadatas, ids): + self._ensure_metadata_columns_exist(category, metadata) + embedding = self.create_embedding(document) + cur.execute( + f""" + INSERT INTO {table_name} (id, document, metadata, embedding) + VALUES (%s, %s %s, %s) + """, + (id_, document, metadata, embedding), + ) + self.connection.commit() + + def query(self, category, query_texts, n_results=5): + embeddings = [self.create_embedding(q) for q in query_texts] + results = [] + self.ensure_table_exists(category) + table_name = self._table_name(category) + with self.connection.cursor() as cur: + for emb in embeddings: + cur.execute( + f""" + SELECT id, document, metadata, embedding <-> %s AS distance + FROM {table_name} + ORDER BY embedding <-> %s + LIMIT %s + """, + (emb, emb, n_results), + ) + results.extend(cur.fetchall()) + return results + + def update(self, category, id_, document=None, metadata=None, embedding=None): + self.ensure_table_exists(category) + table_name = self._table_name(category) + with self.connection.cursor() as cur: + if document: + print("updating document") + print(document) + print(metadata) + # if metadata is a dict, convert it to a JSON string + if isinstance(metadata, dict): + metadata = json.dumps(metadata) + print(id_) + if embedding is None: + embedding = self.create_embedding(document) + cur.execute( + f""" + UPDATE {table_name} + SET document=%s, embedding=%s, metadata=%s + WHERE id=%s + """, + (document, embedding, metadata, id_), + ) + else: + cur.execute( + f""" + UPDATE {table_name} + SET metadata=%s + WHERE id=%s + """, + (metadata, id_), + ) + self.connection.commit() + + def close(self): + self.cur.close() + self.connection.close() diff --git a/agentmemory/tests/__init__.py b/agentmemory/tests/__init__.py index 9de242d..c78d14e 100644 --- a/agentmemory/tests/__init__.py +++ b/agentmemory/tests/__init__.py @@ -1,5 +1,5 @@ -from .helpers import * +# from .helpers import * from .main import * -from .persistence import * -from .events import * -from .clustering import * \ No newline at end of file +# from .persistence import * +# from .events import * +# from .clustering import * \ No newline at end of file diff --git a/agentmemory/tests/main.py b/agentmemory/tests/main.py index cdcc863..844e0fb 100644 --- a/agentmemory/tests/main.py +++ b/agentmemory/tests/main.py @@ -1,3 +1,4 @@ +import time from agentmemory import ( search_memory, get_memory, @@ -98,6 +99,25 @@ def test_count_memories(): wipe_category("test") +def test_delete_memories(): + wipe_category("books") + # create a memory to be deleted + create_memory("books", "Foundation", metadata={"author": "Isaac Asimov"}, id="1") + create_memory("books", "Foundation and Empire", metadata={"author": "Isaac Asimov"}, id="2") + create_memory("books", "Second Foundation", metadata={"author": "Isaac Asimov"}, id="3") + + # assert the memory exists + assert get_memory("books", "1") is not None + + # delete the memory + assert delete_memories("books", "Foundation") + assert delete_memories("books", metadata={"author": "Isaac Asimov"}) + + # assert the memory does not exist anymore + assert get_memory("books", "1") is None + wipe_category("books") + + def test_memory_search_distance(): wipe_category("test") create_memory("test", "cinammon duck cakes") @@ -152,22 +172,3 @@ def test_delete_similar_memories(): memories = get_memories("test") assert len(memories) == 1 wipe_category("test") - - -def test_delete_memories(): - wipe_category("books") - # create a memory to be deleted - create_memory("books", "Foundation", metadata={"author": "Isaac Asimov"}, id="1") - create_memory("books", "Foundation and Empire", metadata={"author": "Isaac Asimov"}, id="2") - create_memory("books", "Second Foundation", metadata={"author": "Isaac Asimov"}, id="3") - - # assert the memory exists - assert get_memory("books", "1") is not None - - # delete the memory - assert delete_memories("books", "Foundation") - assert delete_memories("books", metadata={"author": "Isaac Asimov"}) - - # assert the memory does not exist anymore - assert get_memory("books", "1") is None - wipe_category("books") From 175ccb3caf2165b99f3dc1762cf9aa11929dab22 Mon Sep 17 00:00:00 2001 From: moon Date: Wed, 9 Aug 2023 00:55:35 -0700 Subject: [PATCH 4/5] Tests passing --- README.md | 18 +-- agentmemory/clustering.py | 12 +- agentmemory/helpers.py | 8 +- agentmemory/main.py | 44 +++---- agentmemory/main_no_comments.py | 212 -------------------------------- agentmemory/persistence.py | 4 - agentmemory/postgres.py | 26 ++-- agentmemory/tests/main.py | 51 ++++---- 8 files changed, 85 insertions(+), 290 deletions(-) delete mode 100644 agentmemory/main_no_comments.py diff --git a/README.md b/README.md index 1e80175..9242098 100644 --- a/README.md +++ b/README.md @@ -178,7 +178,7 @@ embedding (array): Embedding of the document. Defaults to None. Use if you alrea #### `create_unique_memory(category, content, metadata={}, similarity=0.95)` -Create a new memory only if there aren't any that are very similar to it. If a similar memory is found, the new memory's "unique" metadata field is set to "False" and it is linked to the existing memory. +Create a new memory only if there aren't any that are very similar to it. If a similar memory is found, the new memory's "novel" metadata field is set to "False" and it is linked to the existing memory. ##### Parameters @@ -193,7 +193,7 @@ None ## Search Memory -#### `search_memory(category, search_text, n_results=5, min_distance=None, max_distance=None, filter_metadata=None, contains_text=None, include_embeddings=True, unique=False)` +#### `search_memory(category, search_text, n_results=5, min_distance=None, max_distance=None, filter_metadata=None, contains_text=None, include_embeddings=True, novel=False)` Search a collection with given query texts. @@ -216,7 +216,7 @@ max_distance (float): Only include memories with this distance threshold maximum 0.1 = most memories will be exluded, 1.0 = no memories will be excluded min_distance (float): Only include memories that are at least this distance 0.0 = No memories will be excluded, 0.9 = most memories will be excluded -unique (bool): Whether to return only unique memories. +novel (bool): Whether to return only novel memories. ``` ##### Returns @@ -263,7 +263,7 @@ dict: The retrieved memory. ## Get Memories -#### `get_memories(category, sort_order="desc", filter_metadata=None, n_results=20, include_embeddings=True, unique=False)` +#### `get_memories(category, sort_order="desc", filter_metadata=None, n_results=20, include_embeddings=True, novel=False)` Retrieve a list of memories from a given category, sorted by ID, with optional filtering. `sort_order` controls whether you get from the beginning or end of the list. @@ -278,7 +278,7 @@ sort_order (str): The sorting order of the memories. Can be 'asc' or 'desc'. Def filter_metadata (dict): Filter to apply on metadata. Defaults to None. n_results (int): The number of results to return. Defaults to 20. include_embeddings (bool): Whether to include the embeddings. Defaults to True. -unique (bool): Whether to return only unique memories. Defaults to False. +novel (bool): Whether to return only novel memories. Defaults to False. ``` ##### Returns @@ -661,7 +661,7 @@ The `cluster` function in `agentmemory.clustering` provides an implementation of ## Function Signature ```python -def cluster(epsilon, min_samples, category, filter_metadata=None, unique=False) +def cluster(epsilon, min_samples, category, filter_metadata=None, novel=False) ``` ## Parameters @@ -670,7 +670,7 @@ def cluster(epsilon, min_samples, category, filter_metadata=None, unique=False) - `min_samples` (int): The number of samples (or total weight) in a neighborhood for a point to be considered as a core point. - `category` (str): The category of the collection to be clustered. - `filter_metadata` (dict, optional): Additional metadata for filtering the memories before clustering. Defaults to None. -- `unique` (bool, optional): Whether to return only unique memories. Defaults to False. +- `novel` (bool, optional): Whether to return only novel memories. Defaults to False. ## Memory Clustering @@ -693,9 +693,9 @@ epsilon = 0.1 min_samples = 3 category = "conversation" filter_metadata = {"speaker": "HAL"} # Optional metadata filter -unique = False # Whether to return only unique memories +novel = False # Whether to return only novel memories -cluster(epsilon, min_samples, category, filter_metadata=filter_metadata, unique=unique) +cluster(epsilon, min_samples, category, filter_metadata=filter_metadata, novel=novel) ``` ## Note diff --git a/agentmemory/clustering.py b/agentmemory/clustering.py index 65a87d2..afe86ba 100644 --- a/agentmemory/clustering.py +++ b/agentmemory/clustering.py @@ -1,11 +1,11 @@ from agentmemory import search_memory, update_memory -def cluster(epsilon, min_samples, category, filter_metadata=None, unique=False): +def cluster(epsilon, min_samples, category, filter_metadata=None, novel=False): """ DBScan clustering. Updates memories directly with their cluster id. """ # Mark all memories as unvisited - memories = search_memory(category, "", n_results=float("inf"), filter_metadata=filter_metadata, unique=unique) + memories = search_memory(category, "", n_results=float("inf"), filter_metadata=filter_metadata, novel=novel) visited = {memory["id"]: False for memory in memories} cluster_id = 0 @@ -16,7 +16,7 @@ def cluster(epsilon, min_samples, category, filter_metadata=None, unique=False): visited[memory_id] = True # Finding neighboring memories based on the epsilon distance threshold - neighbors = search_memory(category, memory["document"], n_results=float("inf"), max_distance=epsilon, filter_metadata=filter_metadata, unique=unique) + neighbors = search_memory(category, memory["document"], n_results=float("inf"), max_distance=epsilon, filter_metadata=filter_metadata, novel=novel) # get the current metadata metadata = memory.get("metadata", {}) @@ -31,10 +31,10 @@ def cluster(epsilon, min_samples, category, filter_metadata=None, unique=False): metadata["cluster"] = str(cluster_id) # Mark the current memory as part of the new cluster update_memory(category, memory_id, metadata=metadata) - _expand_cluster(memory, neighbors, cluster_id, visited, epsilon, min_samples, category, filter_metadata, unique) + _expand_cluster(memory, neighbors, cluster_id, visited, epsilon, min_samples, category, filter_metadata, novel) -def _expand_cluster(memory, neighbors, cluster_id, visited, epsilon, min_samples, category, filter_metadata, unique): +def _expand_cluster(memory, neighbors, cluster_id, visited, epsilon, min_samples, category, filter_metadata, novel): """ Helper function to expand the clusters. """ @@ -45,7 +45,7 @@ def _expand_cluster(memory, neighbors, cluster_id, visited, epsilon, min_samples if not visited[neighbor_id]: visited[neighbor_id] = True - next_neighbors = search_memory(category, neighbor_memory["document"], n_results=float("inf"), max_distance=epsilon, filter_metadata=filter_metadata, unique=unique) + next_neighbors = search_memory(category, neighbor_memory["document"], n_results=float("inf"), max_distance=epsilon, filter_metadata=filter_metadata, novel=novel) if len(next_neighbors) >= min_samples: neighbors += next_neighbors diff --git a/agentmemory/helpers.py b/agentmemory/helpers.py index 0e7b30c..7427723 100644 --- a/agentmemory/helpers.py +++ b/agentmemory/helpers.py @@ -182,7 +182,13 @@ def flatten_arrays(collection): # If no values, continue to next iteration if collection[key] is None: continue - # Flatten the arrays into a single array for each key + + if not isinstance(collection[key], list): + continue + + if not any(isinstance(el, list) for el in collection[key]): + continue + collection[key] = [item for sublist in collection[key] for item in sublist] return collection diff --git a/agentmemory/main.py b/agentmemory/main.py index 33d0918..22e90d9 100644 --- a/agentmemory/main.py +++ b/agentmemory/main.py @@ -45,7 +45,7 @@ def create_memory(category, text, metadata={}, embedding=None, id=None): # if the field is a boolean, convert it to a string for key, value in metadata.items(): if isinstance(value, bool): - print(f"WARNING: Boolean metadata field {key} converted to string") + debug_log(f"WARNING: Boolean metadata field {key} converted to string") metadata[key] = str(value) # insert the document into the collection @@ -82,15 +82,15 @@ def create_unique_memory(category, content, metadata={}, similarity=0.95): max_distance=max_distance, search_text=content, n_results=1, - filter_metadata={"unique": "True"}, + filter_metadata={"novel": "True"}, ) if len(memories) == 0: - metadata["unique"] = "True" + metadata["novel"] = "True" create_memory(category, content, metadata=metadata) return - metadata["unique"] = "False" + metadata["novel"] = "False" metadata["related_to"] = memories[0]["id"] metadata["related_document"] = memories[0]["document"] create_memory(category, content, metadata=metadata) @@ -107,7 +107,7 @@ def search_memory( include_distances=True, max_distance=None, # 0.0 - 1.0 min_distance=None, # 0.0 - 1.0 - unique=False, + novel=False, ): """ Cearch a collection with given query texts. @@ -124,7 +124,7 @@ def search_memory( 0.1 = most memories will be exluded, 1.0 = no memories will be excluded min_distance (float): Only include memories that are at least this distance 0.0 = No memories will be excluded, 0.9 = most memories will be excluded - unique (bool): Only include memories that are marked as unique + novel (bool): Only include memories that are marked as novel Returns: list: List of search results. @@ -159,10 +159,10 @@ def search_memory( filter_metadata = {"$and": filter_metadata} - if unique: + if novel: if filter_metadata is None: filter_metadata = {} - filter_metadata["unique"] = "True" + filter_metadata["novel"] = "True" # perform the query and get the response query = memories.query( @@ -173,7 +173,7 @@ def search_memory( include=include_types, ) - # flatten the arrays in the query response + # if isinstance(query, list): query = flatten_arrays(query) # convert the query response to list and return @@ -212,13 +212,8 @@ def get_memory(category, id, include_embeddings=True): # Get the types to include based on the function parameters include_types = get_include_types(include_embeddings, False) - memory2 = memories.get(limit=1, include=include_types) - print("***** memory no ids returned:") - print(memory2) - # Retrieve the memory with the given ID memory = memories.get(ids=[id], limit=1, include=include_types) - print("***** memory returned:") - print(memory) + memory = chroma_collection_to_list(memory) debug_log(f"Got memory {id} from category {category}", memory) @@ -241,7 +236,7 @@ def get_memories( filter_metadata=None, n_results=20, include_embeddings=True, - unique=False, + novel=False, ): """ Retrieve a list of memories from a given category, sorted by ID, with optional filtering. @@ -252,7 +247,7 @@ def get_memories( filter_metadata (dict, optional): Filter to apply on metadata. Defaults to None. n_results (int, optional): The number of results to return. Defaults to 20. include_embeddings (bool, optional): Whether to include the embeddings. Defaults to True. - unique (bool, optional): Whether to only include memories that are marked as unique. Defaults to False. + novel (bool, optional): Whether to only include memories that are marked as novel. Defaults to False. Returns: list: List of retrieved memories. @@ -284,10 +279,10 @@ def get_memories( filter_metadata = {"$and": filter_metadata} - if unique: + if novel: if filter_metadata is None: filter_metadata = {} - filter_metadata["unique"] = "True" + filter_metadata["novel"] = "True" # Retrieve all memories that meet the given metadata filter memories = memories.get( @@ -298,9 +293,6 @@ def get_memories( # Convert the collection to list format memories = chroma_collection_to_list(memories) - print("memories") - print(memories) - # Sort memories by ID. If sort_order is 'desc', then the reverse parameter will be True, and memories will be sorted in descending order. memories.sort(key=lambda x: x["id"], reverse=sort_order == "desc") @@ -342,7 +334,7 @@ def update_memory(category, id, text=None, metadata=None, embedding=None): # for each key value in metadata -- if the type is boolean, convert it to string for key, value in metadata.items(): if isinstance(value, bool): - print(f"WARNING: Boolean metadata field {key} converted to string") + debug_log(f"WARNING: Boolean metadata field {key} converted to string") metadata[key] = str(value) metadata["updated_at"] = datetime.datetime.now().timestamp() @@ -492,7 +484,7 @@ def memory_exists(category, id, includes_metadata=None): return exists -def count_memories(category, unique=False): +def count_memories(category, novel=False): """ Count the number of memories in a given category. @@ -509,8 +501,8 @@ def count_memories(category, unique=False): # Get or create the collection for the given category memories = get_client().get_or_create_collection(category) - if unique: - memories = memories.get(where={"unique": "True"}) + if novel: + memories = memories.get(where={"novel": "True"}) debug_log(f"Counted memories in {category}: {memories.count()}") diff --git a/agentmemory/main_no_comments.py b/agentmemory/main_no_comments.py deleted file mode 100644 index f15990d..0000000 --- a/agentmemory/main_no_comments.py +++ /dev/null @@ -1,212 +0,0 @@ -import datetime - -from agentmemory.helpers import ( - chroma_collection_to_list, - flatten_arrays, - get_include_types, -) - -from agentmemory.client import get_client - -def create_memory(category, text, metadata={}, embedding=None, id=None): - memories = get_client().get_or_create_collection(category) - metadata["created_at"] = datetime.datetime.now().timestamp() - metadata["updated_at"] = datetime.datetime.now().timestamp() - if id is None: - id = str(memories.count()) - id = id.zfill(16) - for key, value in metadata.items(): - if isinstance(value, bool): - metadata[key] = str(value) - memories.upsert( - ids=[str(id)], - documents=[text], - metadatas=[metadata], - embeddings=[embedding] if embedding is not None else None, - ) - return id - - -def create_unique_memory(category, content, metadata={}, similarity=0.95): - max_distance = 1.0 - similarity - memories = search_memory( - category, - min_distance=0, - max_distance=max_distance, - search_text=content, - n_results=1, - filter_metadata={"unique": "True"}, - ) - if len(memories) == 0: - metadata["unique"] = "True" - create_memory(category, content, metadata=metadata) - return - metadata["unique"] = "False" - metadata["related_to"] = memories[0]["id"] - metadata["related_document"] = memories[0]["document"] - create_memory(category, content, metadata=metadata) - return id - -def search_memory( - category, - search_text, - n_results=5, - filter_metadata=None, - contains_text=None, - include_embeddings=True, - include_distances=True, - max_distance=None, # 0.0 - 1.0 - min_distance=None, # 0.0 - 1.0 - unique=False, -): - if contains_text is not None: - contains_text = {"$contains": contains_text} - memories = get_client().get_or_create_collection(category) - if (memories.count()) == 0: - return [] - n_results = min(n_results, memories.count()) - include_types = get_include_types(include_embeddings, include_distances) - if filter_metadata is not None and len(filter_metadata.keys()) > 1: - # map each key:value in filter_metadata to an object shaped like { "key": { "$eq": "value" } } - filter_metadata = [ - {key: {"$eq": value}} for key, value in filter_metadata.items() - ] - filter_metadata = {"$and": filter_metadata} - if unique: - if filter_metadata is None: - filter_metadata = {} - filter_metadata["unique"] = "True" - query = memories.query( - query_texts=[search_text], - where=filter_metadata, - where_document=contains_text, - n_results=n_results, - include=include_types, - ) - query = flatten_arrays(query) - result_list = chroma_collection_to_list(query) - if min_distance is not None and min_distance > 0: - result_list = [res for res in result_list if res["distance"] >= min_distance] - if max_distance is not None and max_distance < 1.0: - result_list = [res for res in result_list if res["distance"] <= max_distance] - return result_list - - -def get_memory(category, id, include_embeddings=True): - memories = get_client().get_or_create_collection(category) - include_types = get_include_types(include_embeddings, False) - memory = memories.get(ids=[str(id)], limit=1, include=include_types) - if not isinstance(memory, list): - memory = chroma_collection_to_list(memory) - if len(memory) == 0: - return None - return memory[0] - -def get_memories( - category, - sort_order="desc", - contains_text=None, - filter_metadata=None, - n_results=20, - include_embeddings=True, - unique=False, -): - memories = get_client().get_or_create_collection(category) - n_results = min(n_results, memories.count()) - include_types = get_include_types(include_embeddings, False) - where_document = None - if contains_text is not None: - where_document = {"$contains": contains_text} - if filter_metadata is not None and len(filter_metadata.keys()) > 1: - filter_metadata = [ - {key: {"$eq": value}} for key, value in filter_metadata.items() - ] - filter_metadata = {"$and": filter_metadata} - if unique: - if filter_metadata is None: - filter_metadata = {} - filter_metadata["unique"] = "True" - memories = memories.get( - where=filter_metadata, where_document=where_document, include=include_types - ) - if not isinstance(memories, list): - memories = chroma_collection_to_list(memories) - memories.sort(key=lambda x: x["id"], reverse=sort_order == "desc") - memories = memories[:n_results] - return memories - - -def update_memory(category, id, text=None, metadata=None): - memories = get_client().get_or_create_collection(category) - if metadata is None and text is None: - raise Exception("No text or metadata provided") - if metadata is not None: - for key, value in metadata.items(): - if isinstance(value, bool): - metadata[key] = str(value) - metadata["updated_at"] = datetime.datetime.now().timestamp() - documents = [text] if text is not None else None - metadatas = [metadata] if metadata is not None else None - memories.update(ids=[str(id)], documents=documents, metadatas=metadatas) - - -def delete_memory(category, id): - memories = get_client().get_or_create_collection(category) - if memory_exists(category, id) is False: - return - memories.delete(ids=[str(id)]) - -def delete_memories(category, document=None, metadata=None): - memories = get_client().get_or_create_collection(category) - if document is not None: - memories.delete(where_document={"$contains": document}) - if metadata is not None: - memories.delete(where=metadata) - return True - - -def delete_similar_memories(category, content, similarity_threshold=0.95): - memories = search_memory(category, content) - memories_to_delete = [] - if len(memories) > 0: - for memory in memories: - goal_similarity = 1.0 - memory["distance"] - if goal_similarity > similarity_threshold: - memories_to_delete.append(memory["id"]) - else: - break - if len(memories_to_delete) > 0: - for memory in memories_to_delete: - delete_memory(category, memory) - return len(memories_to_delete) > 0 - - -def memory_exists(category, id, includes_metadata=None): - memories = get_client().get_or_create_collection(category) - memory = memories.get(ids=[str(id)], where=includes_metadata, limit=1) - exists = len(memory["ids"]) > 0 - return exists - -def count_memories(category, unique=False): - memories = get_client().get_or_create_collection(category) - if unique: - memories = memories.get(where={"unique": "True"}) - return memories.count() - - -def wipe_category(category): - collection = None - try: - collection = get_client().get_collection(category) - except Exception: - pass - - if collection is not None: - get_client().delete_collection(category) - - -def wipe_all_memories(): - client = get_client() - collections = client.list_collections() - for collection in collections: - client.delete_collection(collection.name) \ No newline at end of file diff --git a/agentmemory/persistence.py b/agentmemory/persistence.py index 76ede30..f2b2524 100644 --- a/agentmemory/persistence.py +++ b/agentmemory/persistence.py @@ -28,11 +28,7 @@ def export_memory_to_json(include_embeddings=True): # Iterate over all collections for collection in collections: - print(collection) collection_name = collection.name - print("collection_name") - print(collection_name) - collections_dict[collection_name] = [] # Get all memories from the current collection diff --git a/agentmemory/postgres.py b/agentmemory/postgres.py index d35074e..56e5a54 100644 --- a/agentmemory/postgres.py +++ b/agentmemory/postgres.py @@ -210,7 +210,6 @@ def get_collection(self, category): return PostgresCollection(category, self) def delete_collection(self, category): - print("deleting collection", category) table_name = self._table_name(category) self.cur.execute(f"DROP TABLE IF EXISTS {table_name}") self.connection.commit() @@ -240,7 +239,7 @@ def insert_memory(self, category, document, metadata={}, embedding=None, id=None return self.cur.fetchone()[0] def create_embedding(self, document): - return self.model.encode(document) + return self.model.encode(document, normalize_embeddings=True) def add(self, category, documents, metadatas, ids): self.ensure_table_exists(category) @@ -260,35 +259,44 @@ def add(self, category, documents, metadatas, ids): def query(self, category, query_texts, n_results=5): embeddings = [self.create_embedding(q) for q in query_texts] - results = [] + results = { + "ids": [], + "documents": [], + "metadatas": [], + "embeddings": [], + "distances": [] + } self.ensure_table_exists(category) table_name = self._table_name(category) with self.connection.cursor() as cur: for emb in embeddings: cur.execute( f""" - SELECT id, document, metadata, embedding <-> %s AS distance + SELECT id, document, metadata, embedding, embedding <-> %s AS distance FROM {table_name} ORDER BY embedding <-> %s LIMIT %s """, (emb, emb, n_results), ) - results.extend(cur.fetchall()) + rows = cur.fetchall() + for row in rows: + results["ids"].append(row[0]) + results["documents"].append(row[1]) + results["metadatas"].append(row[2]) + results["embeddings"].append(row[3]) + results["distances"].append(row[4]) return results + def update(self, category, id_, document=None, metadata=None, embedding=None): self.ensure_table_exists(category) table_name = self._table_name(category) with self.connection.cursor() as cur: if document: - print("updating document") - print(document) - print(metadata) # if metadata is a dict, convert it to a JSON string if isinstance(metadata, dict): metadata = json.dumps(metadata) - print(id_) if embedding is None: embedding = self.create_embedding(document) cur.execute( diff --git a/agentmemory/tests/main.py b/agentmemory/tests/main.py index 844e0fb..f9bb8d9 100644 --- a/agentmemory/tests/main.py +++ b/agentmemory/tests/main.py @@ -39,7 +39,6 @@ def test_memory_deletion(): # Delete memory test create_memory("test", "delete memory test", metadata={"test": "test"}) memories = get_memories("test") - print('memories', memories) memory_id = memories[0]["id"] num_memories = count_memories("test") # test delete_memory @@ -118,36 +117,20 @@ def test_delete_memories(): wipe_category("books") -def test_memory_search_distance(): - wipe_category("test") - create_memory("test", "cinammon duck cakes") - max_dist_limited_memories = search_memory( - "test", "cinammon duck cakes", max_distance=0.1 - ) - - assert len(max_dist_limited_memories) == 1 - wipe_category("test") - - def test_wipe_all_memories(): create_memory("test", "test document") wipe_all_memories() assert count_memories("test") == 0 -def test_create_unique_memory(): +def test_memory_search_distance(): wipe_category("test") - # Test creating a unique memory - create_unique_memory("test", "unique_memory_1") - memories = get_memories("test") - assert len(memories) == 1 - assert memories[0]["metadata"]["unique"] == "True" + create_memory("test", "cinammon duck cakes") + max_dist_limited_memories = search_memory( + "test", "cinammon duck cakes", max_distance=0.1 + ) - # Test creating a non-unique memory similar to the existing one - create_unique_memory("test", "unique_memory_1s") - memories = get_memories("test") - assert len(memories) == 2 - assert memories[0]["metadata"]["unique"] == "False" + assert len(max_dist_limited_memories) == 1 wipe_category("test") @@ -172,3 +155,25 @@ def test_delete_similar_memories(): memories = get_memories("test") assert len(memories) == 1 wipe_category("test") + + +def test_create_unique_memory(): + wipe_category("test") + # Test creating a novel memory + create_unique_memory("test", "unique_memory_1", similarity=0.1) + memories = get_memories("test") + assert len(memories) == 1 + assert memories[0]["metadata"]["novel"] == "True" + + # Test creating a non-novel memory similar to the existing one + create_unique_memory("test", "unique_memory_1", similarity=0.1) + memories = get_memories("test") + assert len(memories) == 2 + assert memories[0]["metadata"]["novel"] == "False" + + # Test creating a non-novel memory similar to the existing one + create_unique_memory("test", "common_object_a", similarity=0.9) + memories = get_memories("test") + assert len(memories) == 3 + assert memories[0]["metadata"]["novel"] == "True" + wipe_category("test") \ No newline at end of file From f6234ca9abf19df415f8c28b84b85c8e943eb155 Mon Sep 17 00:00:00 2001 From: moon Date: Wed, 9 Aug 2023 01:08:49 -0700 Subject: [PATCH 5/5] 0.4.0 --- README.md | 11 ++++++++++- resources/image.jpg | Bin 798989 -> 410352 bytes setup.py | 11 +++++------ 3 files changed, 15 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 9242098..545f17b 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # agentmemory -Easy-to-use agent memory, powered by chromadb +Easy-to-use memory for agents, document search, knowledge graphing and more. @@ -50,6 +50,15 @@ You can enable debugging by passing `debug=True` to most functions, or by settin create_memory("conversation", "I can't do that, Dave.", debug=True) ``` +# Deployment + +CLIENT_TYPE='POSTGRES' | 'CHROMA' +POSTGRES_CONNECTION_STRING=postgres://postgres:MagickDatabase123!@db.xnzvmluhwpbngdufsbrd.supabase.co:6543/postgres + +By default, agentmemory will use a local ChromaDB instance. If you want to use a Postgres instance, you can set the environment variable `CLIENT_TYPE` to `POSTGRES` and set the `POSTGRES_CONNECTION_STRING` environment variable to your Postgres connection string. + +You can deploy an agentmemory-based application to the cloud in minutes using Supabase. Here is a [tutorial](https://supabase.com/blog/openai-embeddings-postgres-vector) and an explanation of [pgvector](https://supabase.com/docs/guides/database/extensions/pgvector). + # Basic Usage Guide ## Importing into your project diff --git a/resources/image.jpg b/resources/image.jpg index 2b0e5799de24196f121ed477d33b22f46b32feef..2c4ae1e0061db81f26c4ff8675b5db46a137683a 100644 GIT binary patch literal 410352 zcmbTc1yo$k7O2^{ySqCy?(Xh1jk|kr2?Tct+PFInjawsuAPMd+5nMv>B!nbG{yXp9 zJG0(kj}R=sjBtIym~-KXdFedj$FW{MBDQGqIifUq(d!%f8PJJ~R1W zcK#Q0|6}tnmix;No?Z^mHh+Egaqx8b%VW=+7#QIA%t%_#93SZF81&35&&=!*;OY9z z-=CS-)6v!s06@n6tM_-bcX?*MXU6t5GEjVGDF6T++xfrP?!VaIG5Fa}06@{(C&bs) z*~Oop-JXM{9B;^mj6$Ke_Q^q z;lJ_&{>|^-Wk;{%=wut<;ZOg!Q0=`vyaRmc{d{cg9qD=g-;MbHIO2b~^S z`Z{_&XJ!1{%3QsipR3!;!PVc@+l$`S>wi_l|A*23%ZI=CuXgB|fNnFtGjWKsGYg7EKp)50(Y{Ox&Qy5oPf`)7U*|L6MuxFEiM{)_16>P-Jvt!Q9G zZy(?r_?Msi#NP`F026=*AO=tXXaP(Bb^s4R03ZsG1jqrD0U7{ZfDynHU zzJMUWOF$GL9*_da0OSG+0cC({Kmz~~3BWX90k8_#27Ck@0?q+ffCs=& 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