From b9ce763fda99b6adebbeead7cb9e945d2cea5e00 Mon Sep 17 00:00:00 2001 From: Sarah Wooders Date: Fri, 3 Nov 2023 16:19:15 -0700 Subject: [PATCH] VectorDB support (pgvector) for archival memory (#226) --- .github/workflows/sarah-test.yml | 40 +++ .github/workflows/tests.yml | 39 +- README.md | 7 +- memgpt/agent.py | 1 + memgpt/cli/cli.py | 38 +- memgpt/cli/cli_config.py | 38 +- memgpt/cli/cli_load.py | 133 ++++++- memgpt/config.py | 60 +++- memgpt/connectors/db.py | 162 +++++++++ memgpt/connectors/local.py | 132 +++++++ memgpt/connectors/storage.py | 86 +++++ memgpt/embeddings.py | 10 +- memgpt/main.py | 36 +- memgpt/memory.py | 134 +++++-- memgpt/persistence_manager.py | 36 +- memgpt/utils.py | 88 ----- poetry.lock | 597 +++++++++++++++++++++---------- pyproject.toml | 4 + tests/__init__.py | 0 tests/constants.py | 1 + tests/test_cli.py | 47 +++ tests/test_load_archival.py | 86 ++++- tests/test_questionary.py | 4 + tests/test_storage.py | 49 +++ tests/utils.py | 36 ++ 25 files changed, 1480 insertions(+), 384 deletions(-) create mode 100644 .github/workflows/sarah-test.yml create mode 100644 memgpt/connectors/db.py create mode 100644 memgpt/connectors/local.py create mode 100644 memgpt/connectors/storage.py create mode 100644 tests/__init__.py create mode 100644 tests/constants.py create mode 100644 tests/test_cli.py create mode 100644 tests/test_storage.py create mode 100644 tests/utils.py diff --git a/.github/workflows/sarah-test.yml b/.github/workflows/sarah-test.yml new file mode 100644 index 00000000..520140f8 --- /dev/null +++ b/.github/workflows/sarah-test.yml @@ -0,0 +1,40 @@ +name: sarah-test + +on: + release: + types: [published] + workflow_dispatch: + +env: + EXAMPLE_VAR: "hello_world" + PGVECTOR_TEST_DB_URL: ${{ secrets.PGVECTOR_TEST_DB_URL }} + OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} +jobs: + test: + + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v2 + + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: 3.10.10 # Set this to your Python version + + - name: Install Poetry + run: | + pip install poetry + - name: Install dependencies using Poetry + run: | + poetry install + - name: Install pexpect for testing the interactive CLI + run: | + poetry add --dev pexpect + - name: Run tests with pytest + env: + EXAMPLE_VAR: "hello_world" + PGVECTOR_TEST_DB_URL: ${{ secrets.PGVECTOR_TEST_DB_URL }} + OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} + run: | + PGVECTOR_TEST_DB_URL=${{ secrets.PGVECTOR_TEST_DB_URL }} OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY }} poetry run pytest -s -vv tests diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 1ab96524..d6f7ab05 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -1,5 +1,9 @@ name: MemGPT tests +env: + PGVECTOR_TEST_DB_URL: ${{ secrets.PGVECTOR_TEST_DB_URL }} + OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} + on: push: branches: [ main ] @@ -11,26 +15,45 @@ jobs: runs-on: ubuntu-latest + env: + PGVECTOR_TEST_DB_URL: ${{ secrets.PGVECTOR_TEST_DB_URL }} + OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} + steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v1 + + - name: Install poetry + run: pipx install poetry - name: Set up Python - uses: actions/setup-python@v2 + uses: actions/setup-python@v4 with: - python-version: 3.10.10 # Set this to your Python version + python-version: "3.10" + cache: "poetry" - - name: Install Poetry + - name: Set Poetry config run: | - pip install poetry + poetry config virtualenvs.in-project false + poetry config virtualenvs.path ~/.virtualenvs - name: Install dependencies using Poetry + env: + PGVECTOR_TEST_DB_URL: ${{ secrets.PGVECTOR_TEST_DB_URL }} + OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} run: | poetry install - - name: Install pexpect for testing the interactive CLI + - name: Set Poetry config + env: + PGVECTOR_TEST_DB_URL: ${{ secrets.PGVECTOR_TEST_DB_URL }} + OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} run: | - poetry add --dev pexpect + poetry config virtualenvs.in-project false + poetry config virtualenvs.path ~/.virtualenvs - name: Run tests with pytest + env: + PGVECTOR_TEST_DB_URL: ${{ secrets.PGVECTOR_TEST_DB_URL }} + OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} run: | - poetry run pytest -s -vv tests + PGVECTOR_TEST_DB_URL=${{ secrets.PGVECTOR_TEST_DB_URL }} OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY }} poetry run pytest -s -vv tests diff --git a/README.md b/README.md index 3435e25d..cc7c37a3 100644 --- a/README.md +++ b/README.md @@ -80,7 +80,6 @@ The `run` command supports the following optional flags (if set, will override c * `--persona`: (str) Name of agent persona to use. * `--model`: (str) LLM model to run [gpt-4, gpt-3.5]. * `--preset`: (str) MemGPT preset to run agent with. -* `--data-source`: (str) Name of data source (loaded with `memgpt load`) to connect to agent. * `--first`: (str) Allow user to sent the first message. * `--debug`: (bool) Show debug logs (default=False) * `--no-verify`: (bool) Bypass message verification (default=False) @@ -88,6 +87,7 @@ The `run` command supports the following optional flags (if set, will override c You can run the following commands in the MemGPT CLI prompt: * `/exit`: Exit the CLI +* `/attach`: Attach a loaded data source to the agent * `/save`: Save a checkpoint of the current agent/conversation state * `/dump`: View the current message log (see the contents of main context) * `/memory`: Print the current contents of agent memory @@ -114,7 +114,10 @@ memgpt list [human/persona] ``` ### Data Sources (i.e. chat with your data) -MemGPT supports pre-loading data into archival memory, so your agent can reference loaded data in your conversations with an agent by specifying the data source with the flag `memgpt run --data-source `. +MemGPT supports pre-loading data into archival memory. You can attach data to your agent (which will place the data in your agent's archival memory) in two ways: + +1. Run `memgpt attach --agent --data-source +2. While chatting with the agent, enter the `/attach` command and select the data source. #### Loading Data We currently support loading from a directory and database dumps. We highly encourage contributions for new data sources, which can be added as a new [CLI data load command](https://github.com/cpacker/MemGPT/blob/main/memgpt/cli/cli_load.py). diff --git a/memgpt/agent.py b/memgpt/agent.py index 65b2f98f..d5687697 100644 --- a/memgpt/agent.py +++ b/memgpt/agent.py @@ -381,6 +381,7 @@ class Agent(object): # load persistence manager filename = os.path.basename(filename).replace(".json", ".persistence.pickle") directory = agent_config.save_persistence_manager_dir() + printd(f"Loading persistence manager from {os.path.join(directory, filename)}") persistence_manager = LocalStateManager.load(os.path.join(directory, filename), agent_config) messages = state["messages"] diff --git a/memgpt/cli/cli.py b/memgpt/cli/cli.py index 20dc1bdb..ac115fdf 100644 --- a/memgpt/cli/cli.py +++ b/memgpt/cli/cli.py @@ -38,7 +38,6 @@ def run( human: str = typer.Option(None, help="Specify human"), model: str = typer.Option(None, help="Specify the LLM model"), preset: str = typer.Option(None, help="Specify preset"), - data_source: str = typer.Option(None, help="Specify data source to attach to agent"), first: bool = typer.Option(False, "--first", help="Use --first to send the first message in the sequence"), strip_ui: bool = typer.Option(False, "--strip_ui", help="Remove all the bells and whistles in CLI output (helpful for testing)"), debug: bool = typer.Option(False, "--debug", help="Use --debug to enable debugging output"), @@ -53,7 +52,6 @@ def run( :param agent: Specify agent name (will load existing state if the agent exists, or create a new one with that name) :param human: Specify human :param model: Specify the LLM model - :param data_source: Specify data source to attach to agent (if new agent is being created) """ @@ -94,7 +92,7 @@ def run( config = MemGPTConfig.load() original_stdout = sys.stdout # unfortunate hack required to suppress confusing print statements from llama index sys.stdout = io.StringIO() - embed_model = embedding_model(config) + embed_model = embedding_model() service_context = ServiceContext.from_defaults(llm=None, embed_model=embed_model, chunk_size=config.embedding_chunk_size) set_global_service_context(service_context) sys.stdout = original_stdout @@ -128,8 +126,8 @@ def run( preset=preset if preset else config.preset, ) - # attach data source to agent - agent_config.attach_data_source(data_source) + ## attach data source to agent + # agent_config.attach_data_source(data_source) # TODO: allow configrable state manager (only local is supported right now) persistence_manager = LocalStateManager(agent_config) # TODO: insert dataset/pre-fill @@ -158,4 +156,32 @@ def run( configure_azure_support() loop = asyncio.get_event_loop() - loop.run_until_complete(run_agent_loop(memgpt_agent, first, no_verify, config, strip_ui)) # TODO: add back no_verify + loop.run_until_complete(run_agent_loop(memgpt_agent, first, no_verify, config)) # TODO: add back no_verify + + +def attach( + agent: str = typer.Option(help="Specify agent to attach data to"), + data_source: str = typer.Option(help="Data source to attach to avent"), +): + # loads the data contained in data source into the agent's memory + from memgpt.connectors.storage import StorageConnector + + agent_config = AgentConfig.load(agent) + config = MemGPTConfig.load() + + # get storage connectors + source_storage = StorageConnector.get_storage_connector(name=data_source) + dest_storage = StorageConnector.get_storage_connector(agent_config=agent_config) + + passages = source_storage.get_all() + for p in passages: + len(p.embedding) == config.embedding_dim, f"Mismatched embedding sizes {len(p.embedding)} != {config.embedding_dim}" + dest_storage.insert_many(passages) + dest_storage.save() + + total_agent_passages = len(dest_storage.get_all()) + + typer.secho( + f"Attached data source {data_source} to agent {agent}, consisting of {len(passages)}. Agent now has {total_agent_passages} embeddings in archival memory.", + fg=typer.colors.GREEN, + ) diff --git a/memgpt/cli/cli_config.py b/memgpt/cli/cli_config.py index b9166998..b03d23b7 100644 --- a/memgpt/cli/cli_config.py +++ b/memgpt/cli/cli_config.py @@ -4,6 +4,7 @@ from prettytable import PrettyTable import typer import os import shutil +from collections import defaultdict # from memgpt.cli import app from memgpt import utils @@ -12,6 +13,7 @@ import memgpt.humans.humans as humans import memgpt.personas.personas as personas from memgpt.config import MemGPTConfig, AgentConfig from memgpt.constants import MEMGPT_DIR +from memgpt.connectors.storage import StorageConnector app = typer.Typer() @@ -33,7 +35,7 @@ def configure(): openai_key = questionary.text("Open AI API keys not found in enviornment - please enter:").ask() # azure credentials - use_azure = questionary.confirm("Do you want to enable MemGPT with Azure?").ask() + use_azure = questionary.confirm("Do you want to enable MemGPT with Azure?", default=False).ask() use_azure_deployment_ids = False if use_azure: # search for key in enviornment @@ -110,6 +112,15 @@ def configure(): # else: # default_agent = None + # Configure archival storage backend + archival_storage_options = ["local", "postgres"] + archival_storage_type = questionary.select("Select storage backend for archival data:", archival_storage_options, default="local").ask() + archival_storage_uri = None + if archival_storage_type == "postgres": + archival_storage_uri = questionary.text( + "Enter postgres connection string (e.g. postgresql+pg8000://{user}:{password}@{ip}:5432/{database}):" + ).ask() + # TODO: allow configuring embedding model config = MemGPTConfig( @@ -125,6 +136,8 @@ def configure(): azure_version=azure_version if use_azure else None, azure_deployment=azure_deployment if use_azure_deployment_ids else None, azure_embedding_deployment=azure_embedding_deployment if use_azure_deployment_ids else None, + archival_storage_type=archival_storage_type, + archival_storage_uri=archival_storage_uri, ) print(f"Saving config to {config.config_path}") config.save() @@ -139,7 +152,7 @@ def list(option: str): for agent_file in utils.list_agent_config_files(): agent_name = os.path.basename(agent_file).replace(".json", "") agent_config = AgentConfig.load(agent_name) - table.add_row([agent_name, agent_config.model, agent_config.persona, agent_config.human, agent_config.data_source]) + table.add_row([agent_name, agent_config.model, agent_config.persona, agent_config.human, ",".join(agent_config.data_sources)]) print(table) elif option == "humans": """List all humans""" @@ -163,10 +176,23 @@ def list(option: str): elif option == "sources": """List all data sources""" table = PrettyTable() - table.field_names = ["Name", "Create Time", "Agents"] - for data_source_file in os.listdir(os.path.join(MEMGPT_DIR, "archival")): - name = os.path.basename(data_source_file) - table.add_row([name, "TODO", "TODO"]) + table.field_names = ["Name", "Location", "Agents"] + config = MemGPTConfig.load() + # TODO: eventually look accross all storage connections + # TODO: add data source stats + source_to_agents = {} + for agent_file in utils.list_agent_config_files(): + agent_name = os.path.basename(agent_file).replace(".json", "") + agent_config = AgentConfig.load(agent_name) + for ds in agent_config.data_sources: + if ds in source_to_agents: + source_to_agents[ds].append(agent_name) + else: + source_to_agents[ds] = [agent_name] + for data_source in StorageConnector.list_loaded_data(): + location = config.archival_storage_type + agents = ",".join(source_to_agents[data_source]) if data_source in source_to_agents else "" + table.add_row([data_source, location, agents]) print(table) else: raise ValueError(f"Unknown option {option}") diff --git a/memgpt/cli/cli_load.py b/memgpt/cli/cli_load.py index a7227cd6..10becc75 100644 --- a/memgpt/cli/cli_load.py +++ b/memgpt/cli/cli_load.py @@ -9,11 +9,77 @@ memgpt load --name [ADDITIONAL ARGS] """ from typing import List +from tqdm import tqdm import typer +from memgpt.embeddings import embedding_model +from memgpt.connectors.storage import StorageConnector, Passage +from memgpt.config import MemGPTConfig + +from llama_index import ( + VectorStoreIndex, + ServiceContext, + StorageContext, + load_index_from_storage, +) app = typer.Typer() +def store_docs(name, docs, show_progress=True): + """Common function for embedding and storing documents""" + storage = StorageConnector.get_storage_connector(name=name) + config = MemGPTConfig.load() + embed_model = embedding_model() + + # use llama index to run embeddings code + service_context = ServiceContext.from_defaults(llm=None, embed_model=embed_model, chunk_size=config.embedding_chunk_size) + index = VectorStoreIndex.from_documents(docs, service_context=service_context, show_progress=True) + embed_dict = index._vector_store._data.embedding_dict + node_dict = index._docstore.docs + + # gather passages + passages = [] + for node_id, node in tqdm(node_dict.items()): + vector = embed_dict[node_id] + node.embedding = vector + text = node.text.replace("\x00", "\uFFFD") # hacky fix for error on null characters + assert ( + len(node.embedding) == config.embedding_dim + ), f"Expected embedding dimension {config.embedding_dim}, got {len(node.embedding)}" + passages.append(Passage(text=text, embedding=vector)) + + # insert into storage + storage.insert_many(passages) + storage.save() + + +@app.command("index") +def load_index( + name: str = typer.Option(help="Name of dataset to load."), dir: str = typer.Option(help="Path to directory containing index.") +): + """Load a LlamaIndex saved VectorIndex into MemGPT""" + # load index data + storage_context = StorageContext.from_defaults(persist_dir=dir) + loaded_index = load_index_from_storage(storage_context) + + # hacky code to extract out passages/embeddings (thanks a lot, llama index) + embed_dict = loaded_index._vector_store._data.embedding_dict + node_dict = loaded_index._docstore.docs + + passages = [] + for node_id, node in node_dict.items(): + vector = embed_dict[node_id] + node.embedding = vector + passages.append(Passage(text=node.text, embedding=vector)) + + # create storage connector + storage = StorageConnector.get_storage_connector(name=name) + + # add and save all passages + storage.insert_many(passages) + storage.save() + + @app.command("directory") def load_directory( name: str = typer.Option(help="Name of dataset to load."), @@ -22,7 +88,6 @@ def load_directory( recursive: bool = typer.Option(False, help="Recursively search for files in directory."), ): from llama_index import SimpleDirectoryReader - from memgpt.utils import get_index, save_index if recursive: assert input_dir is not None, "Must provide input directory if recursive is True." @@ -37,14 +102,10 @@ def load_directory( reader = SimpleDirectoryReader(input_files=input_files) # load docs - print("Loading data...") + print("loading data") docs = reader.load_data() - - # embed docs - print("Indexing documents...") - index = get_index(name, docs) - # save connector information into .memgpt metadata file - save_index(index, name) + print("done loading data") + store_docs(name, docs) @app.command("webpage") @@ -53,15 +114,9 @@ def load_webpage( urls: List[str] = typer.Option(None, help="List of urls to load."), ): from llama_index import SimpleWebPageReader - from memgpt.utils import get_index, save_index docs = SimpleWebPageReader(html_to_text=True).load_data(urls) - - # embed docs - print("Indexing documents...") - index = get_index(docs) - # save connector information into .memgpt metadata file - save_index(index, name) + store_docs(name, docs) @app.command("database") @@ -77,13 +132,12 @@ def load_database( dbname: str = typer.Option(None, help="Database name."), ): from llama_index.readers.database import DatabaseReader - from memgpt.utils import get_index, save_index print(dump_path, scheme) if dump_path is not None: # read from database dump file - from sqlalchemy import create_engine, MetaData + from sqlalchemy import create_engine engine = create_engine(f"sqlite:///{dump_path}") @@ -108,6 +162,47 @@ def load_database( # load data docs = db.load_data(query=query) + store_docs(name, docs) - index = get_index(name, docs) - save_index(index, name) + +@app.command("vector-database") +def load_vector_database( + name: str = typer.Option(help="Name of dataset to load."), + uri: str = typer.Option(help="Database URI."), + table_name: str = typer.Option(help="Name of table containing data."), + text_column: str = typer.Option(help="Name of column containing text."), + embedding_column: str = typer.Option(help="Name of column containing embedding."), +): + """Load pre-computed embeddings into MemGPT from a database.""" + + from sqlalchemy import create_engine, select, MetaData, Table, Inspector + from pgvector.sqlalchemy import Vector + + # connect to db table + engine = create_engine(uri) + metadata = MetaData() + # Create an inspector to inspect the database + inspector = Inspector.from_engine(engine) + table_names = inspector.get_table_names() + assert table_name in table_names, f"Table {table_name} not found in database: tables that exist {table_names}." + + table = Table(table_name, metadata, autoload_with=engine) + + config = MemGPTConfig.load() + + # Prepare a select statement + select_statement = select(table.c[text_column], table.c[embedding_column].cast(Vector(config.embedding_dim))) + + # Execute the query and fetch the results + with engine.connect() as connection: + result = connection.execute(select_statement).fetchall() + + # Convert to a list of tuples (text, embedding) + passages = [] + for text, embedding in result: + passages.append(Passage(text=text, embedding=embedding)) + assert config.embedding_dim == len(embedding), f"Expected embedding dimension {config.embedding_dim}, got {len(embedding)}" + + # insert into storage + storage = StorageConnector.get_storage_connector(name=name) + storage.insert_many(passages) diff --git a/memgpt/config.py b/memgpt/config.py index 8b3f3e98..bd758846 100644 --- a/memgpt/config.py +++ b/memgpt/config.py @@ -65,7 +65,7 @@ class MemGPTConfig: # embedding parameters embedding_model: str = "openai" - embedding_dim: int = 768 + embedding_dim: int = 1536 embedding_chunk_size: int = 300 # number of tokens # database configs: archival @@ -90,8 +90,15 @@ class MemGPTConfig: @classmethod def load(cls) -> "MemGPTConfig": config = configparser.ConfigParser() - if os.path.exists(MemGPTConfig.config_path): - config.read(MemGPTConfig.config_path) + + # allow overriding with env variables + if os.getenv("MEMGPT_CONFIG_PATH"): + config_path = os.getenv("MEMGPT_CONFIG_PATH") + else: + config_path = MemGPTConfig.config_path + + if os.path.exists(config_path): + config.read(config_path) # read config values model = config.get("defaults", "model") @@ -119,6 +126,11 @@ class MemGPTConfig: embedding_dim = config.getint("embedding", "dim") embedding_chunk_size = config.getint("embedding", "chunk_size") + # archival storage + archival_storage_type = config.get("archival_storage", "type") + archival_storage_path = config.get("archival_storage", "path") if config.has_option("archival_storage", "path") else None + archival_storage_uri = config.get("archival_storage", "uri") if config.has_option("archival_storage", "uri") else None + anon_clientid = config.get("client", "anon_clientid") return cls( @@ -137,11 +149,15 @@ class MemGPTConfig: embedding_model=embedding_model, embedding_dim=embedding_dim, embedding_chunk_size=embedding_chunk_size, + archival_storage_type=archival_storage_type, + archival_storage_path=archival_storage_path, + archival_storage_uri=archival_storage_uri, anon_clientid=anon_clientid, + config_path=config_path, ) anon_clientid = MemGPTConfig.generate_uuid() - config = cls(anon_clientid=anon_clientid) + config = cls(anon_clientid=anon_clientid, config_path=config_path) config.save() # save updated config return config @@ -179,18 +195,34 @@ class MemGPTConfig: config.set("embedding", "dim", str(self.embedding_dim)) config.set("embedding", "chunk_size", str(self.embedding_chunk_size)) + # archival storage + config.add_section("archival_storage") + print("archival storage", self.archival_storage_type) + config.set("archival_storage", "type", self.archival_storage_type) + if self.archival_storage_path: + config.set("archival_storage", "path", self.archival_storage_path) + if self.archival_storage_uri: + config.set("archival_storage", "uri", self.archival_storage_uri) + # client config.add_section("client") if not self.anon_clientid: self.anon_clientid = self.generate_uuid() config.set("client", "anon_clientid", self.anon_clientid) + if not os.path.exists(MEMGPT_DIR): + os.makedirs(MEMGPT_DIR, exist_ok=True) with open(self.config_path, "w") as f: config.write(f) @staticmethod def exists(): - return os.path.exists(MemGPTConfig.config_path) + # allow overriding with env variables + if os.getenv("MEMGPT_CONFIG_PATH"): + config_path = os.getenv("MEMGPT_CONFIG_PATH") + else: + config_path = MemGPTConfig.config_path + return os.path.exists(config_path) @staticmethod def create_config_dir(): @@ -209,7 +241,18 @@ class AgentConfig: Configuration for a specific instance of an agent """ - def __init__(self, persona, human, model, preset=DEFAULT_PRESET, name=None, data_source=None, agent_config_path=None, create_time=None): + def __init__( + self, + persona, + human, + model, + preset=DEFAULT_PRESET, + name=None, + data_sources=[], + agent_config_path=None, + create_time=None, + data_source=None, + ): if name is None: self.name = f"agent_{self.generate_agent_id()}" else: @@ -218,8 +261,9 @@ class AgentConfig: self.human = human self.model = model self.preset = preset - self.data_source = data_source + self.data_sources = data_sources self.create_time = create_time if create_time is not None else utils.get_local_time() + self.data_source = None # deprecated # save agent config self.agent_config_path = ( @@ -240,7 +284,7 @@ class AgentConfig: def attach_data_source(self, data_source: str): # TODO: add warning that only once source can be attached # i.e. previous source will be overriden - self.data_source = data_source + self.data_sources.append(data_source) self.save() def save_state_dir(self): diff --git a/memgpt/connectors/db.py b/memgpt/connectors/db.py new file mode 100644 index 00000000..5cefc5e3 --- /dev/null +++ b/memgpt/connectors/db.py @@ -0,0 +1,162 @@ +from pgvector.psycopg import register_vector +from pgvector.sqlalchemy import Vector +import psycopg + + +from sqlalchemy import create_engine, Column, String, BIGINT, select, inspect, text +from sqlalchemy.orm import sessionmaker, mapped_column +from sqlalchemy.ext.declarative import declarative_base +from sqlalchemy.sql import func + +import re +from tqdm import tqdm +from typing import Optional, List +import numpy as np +from tqdm import tqdm + +from memgpt.config import MemGPTConfig +from memgpt.connectors.storage import StorageConnector, Passage +from memgpt.config import AgentConfig, MemGPTConfig +from memgpt.constants import MEMGPT_DIR +from memgpt.utils import printd + +Base = declarative_base() + + +class PassageModel(Base): + """Defines data model for storing Passages (consisting of text, embedding)""" + + __abstract__ = True # this line is necessary + + # Assuming passage_id is the primary key + id = Column(BIGINT, primary_key=True, nullable=False, autoincrement=True) + doc_id = Column(String) + text = Column(String, nullable=False) + embedding = mapped_column(Vector(1536)) # TODO: don't hard-code + # metadata_ = Column(JSON(astext_type=Text())) + + def __repr__(self): + return f" List[Passage]: + session = self.Session() + db_passages = session.query(self.db_model).all() + return [Passage(text=p.text, embedding=p.embedding, doc_id=p.doc_id, passage_id=p.id) for p in db_passages] + + def get(self, id: str) -> Optional[Passage]: + session = self.Session() + db_passage = session.query(self.db_model).get(id) + if db_passage is None: + return None + return Passage(text=db_passage.text, embedding=db_passage.embedding, doc_id=db_passage.doc_id, passage_id=db_passage.passage_id) + + def insert(self, passage: Passage): + session = self.Session() + db_passage = self.db_model(doc_id=passage.doc_id, text=passage.text, embedding=passage.embedding) + session.add(db_passage) + session.commit() + + def insert_many(self, passages: List[Passage], show_progress=True): + session = self.Session() + iterable = tqdm(passages) if show_progress else passages + for passage in iterable: + db_passage = self.db_model(doc_id=passage.doc_id, text=passage.text, embedding=passage.embedding) + session.add(db_passage) + session.commit() + + def query(self, query: str, query_vec: List[float], top_k: int = 10) -> List[Passage]: + session = self.Session() + # Assuming PassageModel.embedding has the capability of computing l2_distance + results = session.scalars(select(self.db_model).order_by(self.db_model.embedding.l2_distance(query_vec)).limit(top_k)).all() + + # Convert the results into Passage objects + passages = [ + Passage(text=result.text, embedding=np.frombuffer(result.embedding), doc_id=result.doc_id, passage_id=result.id) + for result in results + ] + return passages + + def delete(self): + """Drop the passage table from the database.""" + # Bind the engine to the metadata of the base class so that the + # declaratives can be accessed through a DBSession instance + Base.metadata.bind = self.engine + + # Drop the table specified by the PassageModel class + self.db_model.__table__.drop(self.engine) + + def save(self): + # don't need to save + print("Saving db") + return + + @staticmethod + def list_loaded_data(): + config = MemGPTConfig.load() + engine = create_engine(config.archival_storage_uri) + inspector = inspect(engine) + tables = inspector.get_table_names() + tables = [table for table in tables if table.startswith("memgpt_") and not table.startswith("memgpt_agent_")] + tables = [table.replace("memgpt_", "") for table in tables] + return tables + + def sanitize_table_name(self, name: str) -> str: + # Remove leading and trailing whitespace + name = name.strip() + + # Replace spaces and invalid characters with underscores + name = re.sub(r"\s+|\W+", "_", name) + + # Truncate to the maximum identifier length (e.g., 63 for PostgreSQL) + max_length = 63 + if len(name) > max_length: + name = name[:max_length].rstrip("_") + + # Convert to lowercase + name = name.lower() + + return name + + def generate_table_name_agent(self, agent_config: AgentConfig): + return f"memgpt_agent_{self.sanitize_table_name(agent_config.name)}" + + def generate_table_name(self, name: str): + return f"memgpt_{self.sanitize_table_name(name)}" diff --git a/memgpt/connectors/local.py b/memgpt/connectors/local.py new file mode 100644 index 00000000..a916aac1 --- /dev/null +++ b/memgpt/connectors/local.py @@ -0,0 +1,132 @@ +from typing import Optional, List +from memgpt.config import AgentConfig, MemGPTConfig +from tqdm import tqdm +import re +import pickle +import os + + +from typing import List, Optional + +from llama_index import ( + VectorStoreIndex, + EmptyIndex, + ServiceContext, +) +from llama_index.retrievers import VectorIndexRetriever +from llama_index.schema import TextNode + +from memgpt.constants import MEMGPT_DIR +from memgpt.config import MemGPTConfig +from memgpt.connectors.storage import StorageConnector, Passage +from memgpt.config import AgentConfig, MemGPTConfig + + +class LocalStorageConnector(StorageConnector): + + """Local storage connector based on LlamaIndex""" + + def __init__(self, name: Optional[str] = None, agent_config: Optional[AgentConfig] = None): + from memgpt.embeddings import embedding_model + + config = MemGPTConfig.load() + + # TODO: add asserts to avoid both being passed + if name is None: + self.name = agent_config.name + self.save_directory = agent_config.save_agent_index_dir() + else: + self.name = name + self.save_directory = f"{MEMGPT_DIR}/archival/{name}" + + # llama index contexts + self.embed_model = embedding_model() + self.service_context = ServiceContext.from_defaults(llm=None, embed_model=self.embed_model, chunk_size=config.embedding_chunk_size) + + # load/create index + self.save_path = f"{self.save_directory}/nodes.pkl" + if os.path.exists(self.save_path): + self.nodes = pickle.load(open(self.save_path, "rb")) + else: + self.nodes = [] + + # create vectorindex + if len(self.nodes): + self.index = VectorStoreIndex(self.nodes) + else: + self.index = EmptyIndex() + + def get_nodes(self) -> List[TextNode]: + """Get llama index nodes""" + embed_dict = self.index._vector_store._data.embedding_dict + node_dict = self.index._docstore.docs + + nodes = [] + for node_id, node in node_dict.items(): + vector = embed_dict[node_id] + node.embedding = vector + nodes.append(TextNode(text=node.text, embedding=vector)) + return nodes + + def add_nodes(self, nodes: List[TextNode]): + self.nodes += nodes + self.index = VectorStoreIndex(self.nodes) + + def get_all(self) -> List[Passage]: + passages = [] + for node in self.get_nodes(): + assert node.embedding is not None, f"Node embedding is None" + passages.append(Passage(text=node.text, embedding=node.embedding)) + return passages + + def get(self, id: str) -> Passage: + pass + + def insert(self, passage: Passage): + nodes = [TextNode(text=passage.text, embedding=passage.embedding)] + print("nodes", nodes) + self.nodes += nodes + if isinstance(self.index, EmptyIndex): + self.index = VectorStoreIndex(self.nodes, service_context=self.service_context, show_progress=True) + else: + self.index.insert_nodes(nodes) + + def insert_many(self, passages: List[Passage]): + nodes = [TextNode(text=passage.text, embedding=passage.embedding) for passage in passages] + self.nodes += nodes + if isinstance(self.index, EmptyIndex): + self.index = VectorStoreIndex(self.nodes, service_context=self.service_context, show_progress=True) + print("new size", len(self.get_nodes())) + else: + orig_size = len(self.get_nodes()) + self.index.insert_nodes(nodes) + assert len(self.get_nodes()) == orig_size + len( + passages + ), f"expected {orig_size + len(passages)} nodes, got {len(self.get_nodes())} nodes" + + def query(self, query: str, query_vec: List[float], top_k: int = 10) -> List[Passage]: + # TODO: this may be super slow? + # the nice thing about creating this here is that now we can save the persistent storage manager + retriever = VectorIndexRetriever( + index=self.index, # does this get refreshed? + similarity_top_k=top_k, + ) + nodes = retriever.retrieve(query) + results = [Passage(embedding=node.embedding, text=node.text) for node in nodes] + print(results) + return results + + def save(self): + # assert len(self.nodes) == len(self.get_nodes()), f"Expected {len(self.nodes)} nodes, got {len(self.get_nodes())} nodes" + self.nodes = self.get_nodes() + os.makedirs(self.save_directory, exist_ok=True) + pickle.dump(self.nodes, open(self.save_path, "wb")) + print("Saved local", self.save_path) + + @staticmethod + def list_loaded_data(): + sources = [] + for data_source_file in os.listdir(os.path.join(MEMGPT_DIR, "archival")): + name = os.path.basename(data_source_file) + sources.append(name) + return sources diff --git a/memgpt/connectors/storage.py b/memgpt/connectors/storage.py new file mode 100644 index 00000000..7839e5ce --- /dev/null +++ b/memgpt/connectors/storage.py @@ -0,0 +1,86 @@ +""" These classes define storage connectors. + +We originally tried to use Llama Index VectorIndex, but their limited API was extremely problematic. +""" +from typing import Optional, List +import re +import pickle +import os + + +from typing import List, Optional +from abc import abstractmethod +import numpy as np +from tqdm import tqdm + + +from memgpt.config import AgentConfig, MemGPTConfig + + +class Passage: + """A passage is a single unit of memory, and a standard format accross all storage backends. + + It is a string of text with an associated embedding. + """ + + def __init__(self, text: str, embedding: np.ndarray, doc_id: Optional[str] = None, passage_id: Optional[str] = None): + self.text = text + self.embedding = embedding + self.doc_id = doc_id + self.passage_id = passage_id + + def __repr__(self): + return f"Passage(text={self.text}, embedding={self.embedding})" + + +class StorageConnector: + @staticmethod + def get_storage_connector(name: Optional[str] = None, agent_config: Optional[AgentConfig] = None): + from memgpt.connectors.db import PostgresStorageConnector + from memgpt.connectors.local import LocalStorageConnector + + storage_type = MemGPTConfig.load().archival_storage_type + if storage_type == "local": + return LocalStorageConnector(name=name, agent_config=agent_config) + elif storage_type == "postgres": + return PostgresStorageConnector(name=name, agent_config=agent_config) + else: + raise NotImplementedError(f"Storage type {storage_type} not implemented") + + @staticmethod + def list_loaded_data(): + from memgpt.connectors.db import PostgresStorageConnector + from memgpt.connectors.local import LocalStorageConnector + + storage_type = MemGPTConfig.load().archival_storage_type + if storage_type == "local": + return LocalStorageConnector.list_loaded_data() + elif storage_type == "postgres": + return PostgresStorageConnector.list_loaded_data() + else: + raise NotImplementedError(f"Storage type {storage_type} not implemented") + + @abstractmethod + def get_all(self) -> List[Passage]: + pass + + @abstractmethod + def get(self, id: str) -> Passage: + pass + + @abstractmethod + def insert(self, passage: Passage): + pass + + @abstractmethod + def insert_many(self, passages: List[Passage]): + pass + + @abstractmethod + def query(self, query: str, query_vec: List[float], top_k: int = 10) -> List[Passage]: + pass + + @abstractmethod + def save(self): + """Save state of storage connector""" + pass diff --git a/memgpt/embeddings.py b/memgpt/embeddings.py index af95c6b0..20c6040e 100644 --- a/memgpt/embeddings.py +++ b/memgpt/embeddings.py @@ -1,9 +1,15 @@ -from memgpt.config import MemGPTConfig import typer from llama_index.embeddings import OpenAIEmbedding -def embedding_model(config: MemGPTConfig): +def embedding_model(): + """Return LlamaIndex embedding model to use for embeddings""" + + from memgpt.config import MemGPTConfig + + # load config + config = MemGPTConfig.load() + # TODO: use embedding_endpoint in the future if config.model_endpoint == "openai": return OpenAIEmbedding() diff --git a/memgpt/main.py b/memgpt/main.py index ee21272a..ac78e082 100644 --- a/memgpt/main.py +++ b/memgpt/main.py @@ -31,7 +31,7 @@ from memgpt.persistence_manager import ( InMemoryStateManagerWithPreloadedArchivalMemory, InMemoryStateManagerWithFaiss, ) -from memgpt.cli.cli import run +from memgpt.cli.cli import run, attach from memgpt.cli.cli_config import configure, list, add from memgpt.cli.cli_load import app as load_app from memgpt.config import Config, MemGPTConfig, AgentConfig @@ -42,10 +42,12 @@ from memgpt.openai_tools import ( check_azure_embeddings, get_set_azure_env_vars, ) +from memgpt.connectors.storage import StorageConnector import asyncio app = typer.Typer() app.command(name="run")(run) +app.command(name="attach")(attach) app.command(name="configure")(configure) app.command(name="list")(list) app.command(name="add")(add) @@ -410,7 +412,12 @@ async def run_agent_loop(memgpt_agent, first, no_verify=False, cfg=None, strip_u if user_input.startswith("/"): if legacy: # legacy agent save functions (TODO: eventually remove) - if user_input.lower() == "/exit": + if user_input.lower() == "/load" or user_input.lower().startswith("/load "): + command = user_input.strip().split() + filename = command[1] if len(command) > 1 else None + load(memgpt_agent=memgpt_agent, filename=filename) + continue + elif user_input.lower() == "/exit": # autosave save(memgpt_agent=memgpt_agent, cfg=cfg) break @@ -441,10 +448,26 @@ async def run_agent_loop(memgpt_agent, first, no_verify=False, cfg=None, strip_u memgpt_agent.save() continue - if user_input.lower() == "/load" or user_input.lower().startswith("/load "): - command = user_input.strip().split() - filename = command[1] if len(command) > 1 else None - load(memgpt_agent=memgpt_agent, filename=filename) + if user_input.lower() == "/attach": + if legacy: + typer.secho("Error: /attach is not supported in legacy mode.", fg=typer.colors.RED, bold=True) + continue + + # TODO: check if agent already has it + data_source_options = StorageConnector.list_loaded_data() + data_source = await questionary.select("Select data source", choices=data_source_options).ask_async() + + # attach new data + attach(memgpt_agent.config.name, data_source) + + # update agent config + memgpt_agent.config.attach_data_source(data_source) + + # reload agent with new data source + # TODO: maybe make this less ugly... + memgpt_agent.persistence_manager.archival_memory.storage = StorageConnector.get_storage_connector( + agent_config=memgpt_agent.config + ) continue elif user_input.lower() == "/dump": @@ -565,6 +588,7 @@ USER_COMMANDS = [ ("/pop", "undo the last message in the conversation"), ("/heartbeat", "send a heartbeat system message to the agent"), ("/memorywarning", "send a memory warning system message to the agent"), + ("/attach", "attach data source to agent"), ] # if __name__ == "__main__": # diff --git a/memgpt/memory.py b/memgpt/memory.py index 4274d880..6f5efa43 100644 --- a/memgpt/memory.py +++ b/memgpt/memory.py @@ -22,11 +22,20 @@ from llama_index import ( get_response_synthesizer, load_index_from_storage, StorageContext, + Document, ) +from llama_index.node_parser import SimpleNodeParser +from llama_index.node_parser import SimpleNodeParser from llama_index.retrievers import VectorIndexRetriever from llama_index.query_engine import RetrieverQueryEngine from llama_index.indices.postprocessor import SimilarityPostprocessor +from memgpt.embeddings import embedding_model +from memgpt.config import MemGPTConfig + +from memgpt.embeddings import embedding_model +from memgpt.config import MemGPTConfig + class CoreMemory(object): """Held in-context inside the system message @@ -172,11 +181,6 @@ async def a_summarize_messages( class ArchivalMemory(ABC): - @abstractmethod - def __len__(self): - """Define the length of the object. Must be implemented by subclasses.""" - pass - @abstractmethod def insert(self, memory_string): """Insert new archival memory @@ -387,7 +391,7 @@ class DummyArchivalMemoryWithFaiss(DummyArchivalMemory): async def a_insert(self, memory_string, embedding=None): if embedding is None: # Get the embedding - embedding = await async_get_embedding_with_backoff(memory_string, model=self.embedding_model) + embedding = async_get_embedding_with_backoff(memory_string, model=self.embedding_model) return self._insert(memory_string, embedding) def _search(self, query_embedding, query_string, count=None, start=None): @@ -444,11 +448,6 @@ class DummyArchivalMemoryWithFaiss(DummyArchivalMemory): class RecallMemory(ABC): - @abstractmethod - def __len__(self): - """Define the length of the object. Must be implemented by subclasses.""" - pass - @abstractmethod def text_search(self, query_string, count=None, start=None): pass @@ -662,10 +661,11 @@ class LocalArchivalMemory(ArchivalMemory): self.agent_config = agent_config # locate saved index - if self.agent_config.data_source is not None: # connected data source - directory = os.path.join(MEMGPT_DIR, "archival", self.agent_config.data_source) - assert os.path.exists(directory), f"Archival memory database {self.agent_config.data_source} does not exist" - elif self.agent_config.name is not None: + # if self.agent_config.data_source is not None: # connected data source + # directory = f"{MEMGPT_DIR}/archival/{self.agent_config.data_source}" + # assert os.path.exists(directory), f"Archival memory database {self.agent_config.data_source} does not exist" + # elif self.agent_config.name is not None: + if self.agent_config.name is not None: directory = agent_config.save_agent_index_dir() if not os.path.exists(directory): # no existing archival storage @@ -690,17 +690,15 @@ class LocalArchivalMemory(ArchivalMemory): # TODO: have some mechanism for cleanup otherwise will lead to OOM self.cache = {} - def __len__(self): - # TODO FIXME - return 1 - def save(self): """Save the index to disk""" - if self.agent_config.data_source: # update original archival index - # TODO: this corrupts the originally loaded data. do we want to do this? - utils.save_index(self.index, self.agent_config.data_source) - else: - utils.save_agent_index(self.index, self.agent_config) + # if self.agent_config.data_sources: # update original archival index + # # TODO: this corrupts the originally loaded data. do we want to do this? + # utils.save_index(self.index, self.agent_config.data_sources) + # else: + + # don't need to save data source, since we assume data source data is already loaded into the agent index + utils.save_agent_index(self.index, self.agent_config) def insert(self, memory_string): self.index.insert(memory_string) @@ -715,6 +713,7 @@ class LocalArchivalMemory(ArchivalMemory): return self.insert(memory_string) def search(self, query_string, count=None, start=None): + print("searching with local") if self.retriever is None: print("Warning: archival memory is empty") return [], 0 @@ -741,3 +740,90 @@ class LocalArchivalMemory(ArchivalMemory): else: memory_str = self.index.ref_doc_info return f"\n### ARCHIVAL MEMORY ###" + f"\n{memory_str}" + + +class EmbeddingArchivalMemory(ArchivalMemory): + """Archival memory with embedding based search""" + + def __init__(self, agent_config, top_k: Optional[int] = 100): + """Init function for archival memory + + :param archiva_memory_database: name of dataset to pre-fill archival with + :type archival_memory_database: str + """ + from memgpt.connectors.storage import StorageConnector + + self.top_k = top_k + self.agent_config = agent_config + config = MemGPTConfig.load() + + # create embedding model + self.embed_model = embedding_model() + self.embedding_chunk_size = config.embedding_chunk_size + + # create storage backend + self.storage = StorageConnector.get_storage_connector(agent_config=agent_config) + # TODO: have some mechanism for cleanup otherwise will lead to OOM + self.cache = {} + + def save(self): + """Save the index to disk""" + self.storage.save() + + def insert(self, memory_string): + """Embed and save memory string""" + from memgpt.connectors.storage import Passage + + try: + passages = [] + + # create parser + parser = SimpleNodeParser.from_defaults(chunk_size=self.embedding_chunk_size) + + # breakup string into passages + for node in parser.get_nodes_from_documents([Document(text=memory_string)]): + embedding = self.embed_model.get_text_embedding(node.text) + passages.append(Passage(text=node.text, embedding=embedding, doc_id=f"agent_{self.agent_config.name}_memory")) + + # insert passages + self.storage.insert_many(passages) + return True + except Exception as e: + print("Archival insert error", e) + raise e + + def search(self, query_string, count=None, start=None): + """Search query string""" + try: + if query_string not in self.cache: + # self.cache[query_string] = self.retriever.retrieve(query_string) + query_vec = self.embed_model.get_text_embedding(query_string) + self.cache[query_string] = self.storage.query(query_string, query_vec, top_k=self.top_k) + + start = start if start else 0 + count = count if count else self.top_k + end = min(count + start, len(self.cache[query_string])) + + results = self.cache[query_string][start:end] + results = [{"timestamp": get_local_time(), "content": node.text} for node in results] + return results, len(results) + except Exception as e: + print("Archival search error", e) + raise e + + async def a_search(self, query_string, count=None, start=None): + return self.search(query_string, count, start) + + async def a_insert(self, memory_string, embedding=None): + return self.insert(memory_string) + + def __repr__(self) -> str: + limit = 10 + passages = [] + for passage in list(self.storage.get_all())[:limit]: # TODO: only get first 10 + passages.append(str(passage.text)) + memory_str = "\n".join(passages) + return f"\n### ARCHIVAL MEMORY ###" + f"\n{memory_str}" + + def __len__(self): + return len(self.storage.get_all()) diff --git a/memgpt/persistence_manager.py b/memgpt/persistence_manager.py index 51d3b6bf..7899a590 100644 --- a/memgpt/persistence_manager.py +++ b/memgpt/persistence_manager.py @@ -8,7 +8,7 @@ from .memory import ( DummyArchivalMemory, DummyArchivalMemoryWithEmbeddings, DummyArchivalMemoryWithFaiss, - LocalArchivalMemory, + EmbeddingArchivalMemory, ) from .utils import get_local_time, printd @@ -106,34 +106,44 @@ class LocalStateManager(PersistenceManager): """In-memory state manager has nothing to manage, all agents are held in-memory""" recall_memory_cls = DummyRecallMemory - archival_memory_cls = LocalArchivalMemory + archival_memory_cls = EmbeddingArchivalMemory def __init__(self, agent_config: AgentConfig): # Memory held in-state useful for debugging stateful versions self.memory = None self.messages = [] self.all_messages = [] - self.archival_memory = LocalArchivalMemory(agent_config=agent_config) + self.archival_memory = EmbeddingArchivalMemory(agent_config) + self.recall_memory = None self.agent_config = agent_config - @staticmethod - def load(filename, agent_config: AgentConfig): + @classmethod + def load(cls, filename, agent_config: AgentConfig): """ Load a LocalStateManager from a file. """ "" with open(filename, "rb") as f: - manager = pickle.load(f) + data = pickle.load(f) - manager.archival_memory = LocalArchivalMemory(agent_config=agent_config) + manager = cls(agent_config) + manager.all_messages = data["all_messages"] + manager.messages = data["messages"] + manager.recall_memory = data["recall_memory"] + manager.archival_memory = EmbeddingArchivalMemory(agent_config) return manager def save(self, filename): with open(filename, "wb") as fh: - # TODO: fix this hacky solution to pickle the retriever + ## TODO: fix this hacky solution to pickle the retriever self.archival_memory.save() - self.archival_memory = None - pickle.dump(self, fh, protocol=pickle.HIGHEST_PROTOCOL) - - # re-load archival (TODO: dont do this) - self.archival_memory = LocalArchivalMemory(agent_config=self.agent_config) + pickle.dump( + { + "recall_memory": self.recall_memory, + "messages": self.messages, + "all_messages": self.all_messages, + }, + fh, + protocol=pickle.HIGHEST_PROTOCOL, + ) + printd(f"Saved state to {fh}") def init(self, agent): printd(f"Initializing InMemoryStateManager with agent object") diff --git a/memgpt/utils.py b/memgpt/utils.py index cbec9248..ff87fd17 100644 --- a/memgpt/utils.py +++ b/memgpt/utils.py @@ -363,94 +363,6 @@ def estimate_openai_cost(docs): return cost -def get_index(name, docs): - """Index documents - - :param docs: Documents to be embedded - :type docs: List[Document] - """ - from memgpt.config import MemGPTConfig # avoid circular import - from memgpt.embeddings import embedding_model # avoid circular import - - # TODO: configure to work for local - print("Warning: get_index(docs) only supported for OpenAI") - - # check if directory exists - dir = os.path.join(MEMGPT_DIR, "archival", name) - if os.path.exists(dir): - confirm = typer.confirm(typer.style(f"Index with name {name} already exists -- re-index?", fg="yellow"), default=False) - if not confirm: - # return existing index - storage_context = StorageContext.from_defaults(persist_dir=dir) - return load_index_from_storage(storage_context) - - # TODO: support configurable embeddings - # TODO: read from config how to index (open ai vs. local): then embed_mode="local" - - estimated_cost = estimate_openai_cost(docs) - # TODO: prettier cost formatting - confirm = typer.confirm( - typer.style(f"Open AI embedding cost will be approximately ${estimated_cost} - continue?", fg="yellow"), default=True - ) - - if not confirm: - typer.secho("Aborting.", fg="red") - exit() - - # read embedding confirguration - # TODO: in the future, make an IngestData class that loads the config once - config = MemGPTConfig.load() - embed_model = embedding_model(config) - chunk_size = config.embedding_chunk_size - service_context = ServiceContext.from_defaults(embed_model=embed_model, chunk_size=chunk_size) - set_global_service_context(service_context) - - # index documents - index = VectorStoreIndex.from_documents(docs) - return index - - -def save_agent_index(index, agent_config): - """Save agent index inside of ~/.memgpt/agents/ - - :param index: Index to save - :type index: VectorStoreIndex - :param agent_name: Name of agent that the archival memory belonds to - :type agent_name: str - """ - dir = agent_config.save_agent_index_dir() - os.makedirs(dir, exist_ok=True) - index.storage_context.persist(dir) - - -def save_index(index, name): - """Save index ~/.memgpt/archival/ to load into agents - - :param index: Index to save - :type index: VectorStoreIndex - :param name: Name of index - :type name: str - """ - # save - # TODO: load directory from config - # TODO: save to vectordb/local depending on config - - dir = os.path.join(MEMGPT_DIR, "archival", name) - - ## Avoid overwriting - ## check if directory exists - # if os.path.exists(dir): - # confirm = typer.confirm(typer.style(f"Index with name {name} already exists -- overwrite?", fg="red"), default=False) - # if not confirm: - # typer.secho("Aborting.", fg="red") - # exit() - - # create directory, even if it already exists - os.makedirs(dir, exist_ok=True) - index.storage_context.persist(dir) - print(dir) - - def list_agent_config_files(): """List all agents config files""" return os.listdir(os.path.join(MEMGPT_DIR, "agents")) diff --git a/poetry.lock b/poetry.lock index fd2c27b9..982683f9 100644 --- a/poetry.lock +++ b/poetry.lock @@ -261,102 +261,102 @@ files = [ [[package]] name = "charset-normalizer" -version = "3.3.1" +version = "3.3.2" description = "The Real First Universal Charset Detector. 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+36,10 @@ llama-index = "^0.8.53.post3" setuptools = "^68.2.2" datasets = "^2.14.6" prettytable = "^3.9.0" +pgvector = "^0.2.3" +psycopg = "^3.1.12" +psycopg-binary = "^3.1.12" +psycopg2-binary = "^2.9.9" [build-system] diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/constants.py b/tests/constants.py new file mode 100644 index 00000000..e1832cbd --- /dev/null +++ b/tests/constants.py @@ -0,0 +1 @@ +TIMEOUT = 30 # seconds diff --git a/tests/test_cli.py b/tests/test_cli.py new file mode 100644 index 00000000..18f34e7a --- /dev/null +++ b/tests/test_cli.py @@ -0,0 +1,47 @@ +import subprocess +import sys + +subprocess.check_call([sys.executable, "-m", "pip", "install", "pexpect"]) +import pexpect + +from .constants import TIMEOUT +from .utils import configure_memgpt + + +def test_configure_memgpt(): + configure_memgpt() + + +def test_save_load(): + configure_memgpt() + child = pexpect.spawn("memgpt run --agent test_save_load --first --strip_ui") + + child.expect("Enter your message:", timeout=TIMEOUT) + child.sendline() + + child.expect("Empty input received. Try again!", timeout=TIMEOUT) + child.sendline("/save") + + child.expect("Saved local", timeout=TIMEOUT) + child.expect("Enter your message:", timeout=TIMEOUT) + child.sendline("/exit") + + child.expect(pexpect.EOF, timeout=TIMEOUT) # Wait for child to exit + child.close() + assert child.isalive() is False, "CLI should have terminated." + assert child.exitstatus == 0, "CLI did not exit cleanly." + + child = pexpect.spawn("memgpt run --agent test_save_load --first --strip_ui") + child.expect("Using existing agent test_save_load", timeout=TIMEOUT) + child.expect("Enter your message:", timeout=TIMEOUT) + child.sendline("/exit") + child.expect(pexpect.EOF, timeout=TIMEOUT) # Wait for child to exit + child.close() + assert child.isalive() is False, "CLI should have terminated." + assert child.exitstatus == 0, "CLI did not exit cleanly." + + +if __name__ == "__main__": + test_configure_memgpt() + test_save_load() + # test_legacy_cli_sequence() diff --git a/tests/test_load_archival.py b/tests/test_load_archival.py index 3b6a7703..d21eb7c2 100644 --- a/tests/test_load_archival.py +++ b/tests/test_load_archival.py @@ -1,11 +1,13 @@ # import tempfile # import asyncio -# import os -# import asyncio -# from datasets import load_dataset +import os + +# import asyncio +from datasets import load_dataset + +import memgpt +from memgpt.cli.cli_load import load_directory, load_database, load_webpage -# import memgpt -# from memgpt.cli.cli_load import load_directory, load_database, load_webpage # import memgpt.presets as presets # import memgpt.personas.personas as personas # import memgpt.humans.humans as humans @@ -16,6 +18,80 @@ # import memgpt.interface # for printing to terminal +def test_postgres(): + return + + # override config path with enviornment variable + # TODO: make into temporary file + os.environ["MEMGPT_CONFIG_PATH"] = "test_config.cfg" + print("env", os.getenv("MEMGPT_CONFIG_PATH")) + config = memgpt.config.MemGPTConfig(archival_storage_type="postgres", config_path=os.getenv("MEMGPT_CONFIG_PATH")) + print(config) + config.save() + # exit() + + name = "tmp_hf_dataset2" + + dataset = load_dataset("MemGPT/example_short_stories") + + cache_dir = os.getenv("HF_DATASETS_CACHE") + if cache_dir is None: + # Construct the default path if the environment variable is not set. + cache_dir = os.path.join(os.path.expanduser("~"), ".cache", "huggingface", "datasets") + + load_directory( + name=name, + input_dir=cache_dir, + recursive=True, + ) + + +def test_chroma(): + return + + subprocess.check_call([sys.executable, "-m", "pip", "install", "chromadb"]) + import chromadb # Try to import again after installing + + # override config path with enviornment variable + # TODO: make into temporary file + os.environ["MEMGPT_CONFIG_PATH"] = "test_config.cfg" + print("env", os.getenv("MEMGPT_CONFIG_PATH")) + config = memgpt.config.MemGPTConfig(archival_storage_type="chroma", config_path=os.getenv("MEMGPT_CONFIG_PATH")) + print(config) + config.save() + # exit() + + name = "tmp_hf_dataset" + + dataset = load_dataset("MemGPT/example_short_stories") + + cache_dir = os.getenv("HF_DATASETS_CACHE") + if cache_dir is None: + # Construct the default path if the environment variable is not set. + cache_dir = os.path.join(os.path.expanduser("~"), ".cache", "huggingface", "datasets") + + config = memgpt.config.MemGPTConfig(archival_storage_type="chroma") + + load_directory( + name=name, + input_dir=cache_dir, + recursive=True, + ) + + # index = memgpt.embeddings.Index(name) + + ## query chroma + ##chroma_client = chromadb.Client() + # chroma_client = chromadb.PersistentClient(path="/Users/sarahwooders/repos/MemGPT/chromadb") + # collection = chroma_client.get_collection(name=name) + # results = collection.query( + # query_texts=["cinderella be getting sick"], + # n_results=2 + # ) + # print(results) + # assert len(results) == 2, f"Expected 2 results, but got {len(results)}" + + def test_load_directory(): return # downloading hugging face dataset (if does not exist) diff --git a/tests/test_questionary.py b/tests/test_questionary.py index fdea58c0..7ad67d16 100644 --- a/tests/test_questionary.py +++ b/tests/test_questionary.py @@ -1,3 +1,7 @@ +import subprocess +import sys + +subprocess.check_call([sys.executable, "-m", "pip", "install", "pexpect"]) import pexpect diff --git a/tests/test_storage.py b/tests/test_storage.py new file mode 100644 index 00000000..f15de2c9 --- /dev/null +++ b/tests/test_storage.py @@ -0,0 +1,49 @@ +import os +import subprocess +import sys + +subprocess.check_call( + [sys.executable, "-m", "pip", "install", "pgvector", "psycopg", "psycopg2-binary"] +) # , "psycopg_binary"]) # "psycopg", "libpq-dev"]) +import pgvector # Try to import again after installing + +from memgpt.connectors.storage import StorageConnector, Passage +from memgpt.connectors.db import PostgresStorageConnector +from memgpt.embeddings import embedding_model +from memgpt.config import MemGPTConfig, AgentConfig + +import argparse + + +def test_postgres(): + config = MemGPTConfig() + config.archival_storage_uri = os.getenv("PGVECTOR_TEST_DB_URL") # the URI for a postgres DB w/ the pgvector extension + assert config.archival_storage_uri is not None + config.archival_storage_uri.replace("postgres://", "postgresql://") # https://stackoverflow.com/a/64698899 + config.save() + print(config) + + embed_model = embedding_model() + + passage = ["This is a test passage", "This is another test passage", "Cinderella wept"] + + db = PostgresStorageConnector(name="test2") + + for passage in passage: + db.insert(Passage(text=passage, embedding=embed_model.get_text_embedding(passage))) + + print(db.get_all()) + + query = "why was she crying" + query_vec = embed_model.get_text_embedding(query) + res = db.query(None, query_vec, top_k=2) + + assert len(res) == 2, f"Expected 2 results, got {len(res)}" + assert "wept" in res[0].text, f"Expected 'wept' in results, but got {res[0].text}" + + print("deleting...") + db.delete() + print("...finished") + + +test_postgres() diff --git a/tests/utils.py b/tests/utils.py new file mode 100644 index 00000000..ca7be2d0 --- /dev/null +++ b/tests/utils.py @@ -0,0 +1,36 @@ +import pexpect + +from .constants import TIMEOUT + + +def configure_memgpt(enable_openai=True, enable_azure=False): + child = pexpect.spawn("memgpt configure") + + child.expect("Do you want to enable MemGPT with Open AI?", timeout=TIMEOUT) + if enable_openai: + child.sendline("y") + else: + child.sendline("n") + + child.expect("Do you want to enable MemGPT with Azure?", timeout=TIMEOUT) + if enable_azure: + child.sendline("y") + else: + child.sendline("n") + + child.expect("Select default preset:", timeout=TIMEOUT) + child.sendline() + + child.expect("Select default persona:", timeout=TIMEOUT) + child.sendline() + + child.expect("Select default human:", timeout=TIMEOUT) + child.sendline() + + child.expect("Select storage backend for archival data:", timeout=TIMEOUT) + child.sendline() + + child.expect(pexpect.EOF, timeout=TIMEOUT) # Wait for child to exit + child.close() + assert child.isalive() is False, "CLI should have terminated." + assert child.exitstatus == 0, "CLI did not exit cleanly."