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cli_config.py
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cli_config.py
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import ast
import builtins
import os
from enum import Enum
from typing import Annotated, List, Optional
import questionary
import typer
from prettytable.colortable import ColorTable, Themes
from tqdm import tqdm
from memgpt import utils
from memgpt.config import MemGPTConfig
from memgpt.constants import LLM_MAX_TOKENS, MEMGPT_DIR
from memgpt.credentials import SUPPORTED_AUTH_TYPES, MemGPTCredentials
from memgpt.llm_api.anthropic import (
anthropic_get_model_list,
antropic_get_model_context_window,
)
from memgpt.llm_api.azure_openai import azure_openai_get_model_list
from memgpt.llm_api.cohere import (
COHERE_VALID_MODEL_LIST,
cohere_get_model_context_window,
cohere_get_model_list,
)
from memgpt.llm_api.google_ai import (
google_ai_get_model_context_window,
google_ai_get_model_list,
)
from memgpt.llm_api.llm_api_tools import LLM_API_PROVIDER_OPTIONS
from memgpt.llm_api.openai import openai_get_model_list
from memgpt.local_llm.constants import (
DEFAULT_ENDPOINTS,
DEFAULT_OLLAMA_MODEL,
DEFAULT_WRAPPER_NAME,
)
from memgpt.local_llm.utils import get_available_wrappers
from memgpt.schemas.embedding_config import EmbeddingConfig
from memgpt.schemas.llm_config import LLMConfig
from memgpt.server.utils import shorten_key_middle
app = typer.Typer()
def get_azure_credentials():
creds = dict(
azure_key=os.getenv("AZURE_OPENAI_KEY"),
azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
azure_version=os.getenv("AZURE_OPENAI_VERSION"),
azure_deployment=os.getenv("AZURE_OPENAI_DEPLOYMENT"),
azure_embedding_deployment=os.getenv("AZURE_OPENAI_EMBEDDING_DEPLOYMENT"),
)
# embedding endpoint and version default to non-embedding
creds["azure_embedding_endpoint"] = os.getenv("AZURE_OPENAI_EMBEDDING_ENDPOINT", creds["azure_endpoint"])
creds["azure_embedding_version"] = os.getenv("AZURE_OPENAI_EMBEDDING_VERSION", creds["azure_version"])
return creds
def get_openai_credentials() -> Optional[str]:
openai_key = os.getenv("OPENAI_API_KEY", None)
return openai_key
def get_google_ai_credentials() -> Optional[str]:
google_ai_key = os.getenv("GOOGLE_AI_API_KEY", None)
return google_ai_key
def configure_llm_endpoint(config: MemGPTConfig, credentials: MemGPTCredentials):
# configure model endpoint
model_endpoint_type, model_endpoint = None, None
# get default
default_model_endpoint_type = config.default_llm_config.model_endpoint_type if config.default_embedding_config else None
if (
config.default_llm_config
and config.default_llm_config.model_endpoint_type is not None
and config.default_llm_config.model_endpoint_type not in [provider for provider in LLM_API_PROVIDER_OPTIONS if provider != "local"]
): # local model
default_model_endpoint_type = "local"
provider = questionary.select(
"Select LLM inference provider:",
choices=LLM_API_PROVIDER_OPTIONS,
default=default_model_endpoint_type,
).ask()
if provider is None:
raise KeyboardInterrupt
# set: model_endpoint_type, model_endpoint
if provider == "openai":
# check for key
if credentials.openai_key is None:
# allow key to get pulled from env vars
openai_api_key = os.getenv("OPENAI_API_KEY", None)
# if we still can't find it, ask for it as input
if openai_api_key is None:
while openai_api_key is None or len(openai_api_key) == 0:
# Ask for API key as input
openai_api_key = questionary.password(
"Enter your OpenAI API key (starts with 'sk-', see https://platform.openai.com/api-keys):"
).ask()
if openai_api_key is None:
raise KeyboardInterrupt
credentials.openai_key = openai_api_key
credentials.save()
else:
# Give the user an opportunity to overwrite the key
openai_api_key = None
default_input = (
shorten_key_middle(credentials.openai_key) if credentials.openai_key.startswith("sk-") else credentials.openai_key
)
openai_api_key = questionary.password(
"Enter your OpenAI API key (starts with 'sk-', see https://platform.openai.com/api-keys):",
default=default_input,
).ask()
if openai_api_key is None:
raise KeyboardInterrupt
# If the user modified it, use the new one
if openai_api_key != default_input:
credentials.openai_key = openai_api_key
credentials.save()
model_endpoint_type = "openai"
model_endpoint = "https://api.openai.com/v1"
model_endpoint = questionary.text("Override default endpoint:", default=model_endpoint).ask()
if model_endpoint is None:
raise KeyboardInterrupt
provider = "openai"
elif provider == "azure":
# check for necessary vars
azure_creds = get_azure_credentials()
if not all([azure_creds["azure_key"], azure_creds["azure_endpoint"], azure_creds["azure_version"]]):
raise ValueError(
"Missing environment variables for Azure (see https://memgpt.readme.io/docs/endpoints#azure-openai). Please set then run `memgpt configure` again."
)
else:
credentials.azure_key = azure_creds["azure_key"]
credentials.azure_version = azure_creds["azure_version"]
credentials.azure_endpoint = azure_creds["azure_endpoint"]
if "azure_deployment" in azure_creds:
credentials.azure_deployment = azure_creds["azure_deployment"]
credentials.azure_embedding_version = azure_creds["azure_embedding_version"]
credentials.azure_embedding_endpoint = azure_creds["azure_embedding_endpoint"]
if "azure_embedding_deployment" in azure_creds:
credentials.azure_embedding_deployment = azure_creds["azure_embedding_deployment"]
credentials.save()
model_endpoint_type = "azure"
model_endpoint = azure_creds["azure_endpoint"]
elif provider == "google_ai":
# check for key
if credentials.google_ai_key is None:
# allow key to get pulled from env vars
google_ai_key = get_google_ai_credentials()
# if we still can't find it, ask for it as input
if google_ai_key is None:
while google_ai_key is None or len(google_ai_key) == 0:
# Ask for API key as input
google_ai_key = questionary.password(
"Enter your Google AI (Gemini) API key (see https://aistudio.google.com/app/apikey):"
).ask()
if google_ai_key is None:
raise KeyboardInterrupt
credentials.google_ai_key = google_ai_key
else:
# Give the user an opportunity to overwrite the key
google_ai_key = None
default_input = shorten_key_middle(credentials.google_ai_key)
google_ai_key = questionary.password(
"Enter your Google AI (Gemini) API key (see https://aistudio.google.com/app/apikey):",
default=default_input,
).ask()
if google_ai_key is None:
raise KeyboardInterrupt
# If the user modified it, use the new one
if google_ai_key != default_input:
credentials.google_ai_key = google_ai_key
default_input = os.getenv("GOOGLE_AI_SERVICE_ENDPOINT", None)
if default_input is None:
default_input = "generativelanguage"
google_ai_service_endpoint = questionary.text(
"Enter your Google AI (Gemini) service endpoint (see https://ai.google.dev/api/rest):",
default=default_input,
).ask()
credentials.google_ai_service_endpoint = google_ai_service_endpoint
# write out the credentials
credentials.save()
model_endpoint_type = "google_ai"
elif provider == "anthropic":
# check for key
if credentials.anthropic_key is None:
# allow key to get pulled from env vars
anthropic_api_key = os.getenv("ANTHROPIC_API_KEY", None)
# if we still can't find it, ask for it as input
if anthropic_api_key is None:
while anthropic_api_key is None or len(anthropic_api_key) == 0:
# Ask for API key as input
anthropic_api_key = questionary.password(
"Enter your Anthropic API key (starts with 'sk-', see https://console.anthropic.com/settings/keys):"
).ask()
if anthropic_api_key is None:
raise KeyboardInterrupt
credentials.anthropic_key = anthropic_api_key
credentials.save()
else:
# Give the user an opportunity to overwrite the key
anthropic_api_key = None
default_input = (
shorten_key_middle(credentials.anthropic_key) if credentials.anthropic_key.startswith("sk-") else credentials.anthropic_key
)
anthropic_api_key = questionary.password(
"Enter your Anthropic API key (starts with 'sk-', see https://console.anthropic.com/settings/keys):",
default=default_input,
).ask()
if anthropic_api_key is None:
raise KeyboardInterrupt
# If the user modified it, use the new one
if anthropic_api_key != default_input:
credentials.anthropic_key = anthropic_api_key
credentials.save()
model_endpoint_type = "anthropic"
model_endpoint = "https://api.anthropic.com/v1"
model_endpoint = questionary.text("Override default endpoint:", default=model_endpoint).ask()
if model_endpoint is None:
raise KeyboardInterrupt
provider = "anthropic"
elif provider == "cohere":
# check for key
if credentials.cohere_key is None:
# allow key to get pulled from env vars
cohere_api_key = os.getenv("COHERE_API_KEY", None)
# if we still can't find it, ask for it as input
if cohere_api_key is None:
while cohere_api_key is None or len(cohere_api_key) == 0:
# Ask for API key as input
cohere_api_key = questionary.password("Enter your Cohere API key (see https://dashboard.cohere.com/api-keys):").ask()
if cohere_api_key is None:
raise KeyboardInterrupt
credentials.cohere_key = cohere_api_key
credentials.save()
else:
# Give the user an opportunity to overwrite the key
cohere_api_key = None
default_input = (
shorten_key_middle(credentials.cohere_key) if credentials.cohere_key.startswith("sk-") else credentials.cohere_key
)
cohere_api_key = questionary.password(
"Enter your Cohere API key (see https://dashboard.cohere.com/api-keys):",
default=default_input,
).ask()
if cohere_api_key is None:
raise KeyboardInterrupt
# If the user modified it, use the new one
if cohere_api_key != default_input:
credentials.cohere_key = cohere_api_key
credentials.save()
model_endpoint_type = "cohere"
model_endpoint = "https://api.cohere.ai/v1"
model_endpoint = questionary.text("Override default endpoint:", default=model_endpoint).ask()
if model_endpoint is None:
raise KeyboardInterrupt
provider = "cohere"
else: # local models
# backend_options_old = ["webui", "webui-legacy", "llamacpp", "koboldcpp", "ollama", "lmstudio", "lmstudio-legacy", "vllm", "openai"]
backend_options = builtins.list(DEFAULT_ENDPOINTS.keys())
# assert backend_options_old == backend_options, (backend_options_old, backend_options)
default_model_endpoint_type = None
if config.default_llm_config and config.default_llm_config.model_endpoint_type in backend_options:
# set from previous config
default_model_endpoint_type = config.default_llm_config.model_endpoint_type
model_endpoint_type = questionary.select(
"Select LLM backend (select 'openai' if you have an OpenAI compatible proxy):",
backend_options,
default=default_model_endpoint_type,
).ask()
if model_endpoint_type is None:
raise KeyboardInterrupt
# set default endpoint
# if OPENAI_API_BASE is set, assume that this is the IP+port the user wanted to use
default_model_endpoint = os.getenv("OPENAI_API_BASE")
# if OPENAI_API_BASE is not set, try to pull a default IP+port format from a hardcoded set
if default_model_endpoint is None:
if model_endpoint_type in DEFAULT_ENDPOINTS:
default_model_endpoint = DEFAULT_ENDPOINTS[model_endpoint_type]
model_endpoint = questionary.text("Enter default endpoint:", default=default_model_endpoint).ask()
if model_endpoint is None:
raise KeyboardInterrupt
while not utils.is_valid_url(model_endpoint):
typer.secho(f"Endpoint must be a valid address", fg=typer.colors.YELLOW)
model_endpoint = questionary.text("Enter default endpoint:", default=default_model_endpoint).ask()
if model_endpoint is None:
raise KeyboardInterrupt
elif config.default_llm_config and config.default_llm_config.model_endpoint:
model_endpoint = questionary.text("Enter default endpoint:", default=config.default_llm_config.model_endpoint).ask()
if model_endpoint is None:
raise KeyboardInterrupt
while not utils.is_valid_url(model_endpoint):
typer.secho(f"Endpoint must be a valid address", fg=typer.colors.YELLOW)
model_endpoint = questionary.text("Enter default endpoint:", default=config.default_llm_config.model_endpoint).ask()
if model_endpoint is None:
raise KeyboardInterrupt
else:
# default_model_endpoint = None
model_endpoint = None
model_endpoint = questionary.text("Enter default endpoint:").ask()
if model_endpoint is None:
raise KeyboardInterrupt
while not utils.is_valid_url(model_endpoint):
typer.secho(f"Endpoint must be a valid address", fg=typer.colors.YELLOW)
model_endpoint = questionary.text("Enter default endpoint:").ask()
if model_endpoint is None:
raise KeyboardInterrupt
else:
model_endpoint = default_model_endpoint
assert model_endpoint, f"Environment variable OPENAI_API_BASE must be set."
return model_endpoint_type, model_endpoint
def get_model_options(
credentials: MemGPTCredentials,
model_endpoint_type: str,
model_endpoint: str,
filter_list: bool = True,
filter_prefix: str = "gpt-",
) -> list:
try:
if model_endpoint_type == "openai":
if credentials.openai_key is None:
raise ValueError("Missing OpenAI API key")
fetched_model_options_response = openai_get_model_list(url=model_endpoint, api_key=credentials.openai_key)
# Filter the list for "gpt" models only
if filter_list:
model_options = [obj["id"] for obj in fetched_model_options_response["data"] if obj["id"].startswith(filter_prefix)]
else:
model_options = [obj["id"] for obj in fetched_model_options_response["data"]]
elif model_endpoint_type == "azure":
if credentials.azure_key is None:
raise ValueError("Missing Azure key")
if credentials.azure_version is None:
raise ValueError("Missing Azure version")
fetched_model_options_response = azure_openai_get_model_list(
url=model_endpoint, api_key=credentials.azure_key, api_version=credentials.azure_version
)
# Filter the list for "gpt" models only
if filter_list:
model_options = [obj["id"] for obj in fetched_model_options_response["data"] if obj["id"].startswith(filter_prefix)]
else:
model_options = [obj["id"] for obj in fetched_model_options_response["data"]]
elif model_endpoint_type == "google_ai":
if credentials.google_ai_key is None:
raise ValueError("Missing Google AI API key")
if credentials.google_ai_service_endpoint is None:
raise ValueError("Missing Google AI service endpoint")
model_options = google_ai_get_model_list(
service_endpoint=credentials.google_ai_service_endpoint, api_key=credentials.google_ai_key
)
model_options = [str(m["name"]) for m in model_options]
model_options = [mo[len("models/") :] if mo.startswith("models/") else mo for mo in model_options]
# TODO remove manual filtering for gemini-pro
model_options = [mo for mo in model_options if str(mo).startswith("gemini") and "-pro" in str(mo)]
# model_options = ["gemini-pro"]
elif model_endpoint_type == "anthropic":
if credentials.anthropic_key is None:
raise ValueError("Missing Anthropic API key")
fetched_model_options = anthropic_get_model_list(url=model_endpoint, api_key=credentials.anthropic_key)
model_options = [obj["name"] for obj in fetched_model_options]
elif model_endpoint_type == "cohere":
if credentials.cohere_key is None:
raise ValueError("Missing Cohere API key")
fetched_model_options = cohere_get_model_list(url=model_endpoint, api_key=credentials.cohere_key)
model_options = [obj for obj in fetched_model_options]
else:
# Attempt to do OpenAI endpoint style model fetching
# TODO support local auth with api-key header
if credentials.openllm_auth_type == "bearer_token":
api_key = credentials.openllm_key
else:
api_key = None
fetched_model_options_response = openai_get_model_list(url=model_endpoint, api_key=api_key, fix_url=True)
model_options = [obj["id"] for obj in fetched_model_options_response["data"]]
# NOTE no filtering of local model options
# list
return model_options
except:
raise Exception(f"Failed to get model list from {model_endpoint}")
def configure_model(config: MemGPTConfig, credentials: MemGPTCredentials, model_endpoint_type: str, model_endpoint: str):
# set: model, model_wrapper
model, model_wrapper = None, None
if model_endpoint_type == "openai" or model_endpoint_type == "azure":
# Get the model list from the openai / azure endpoint
hardcoded_model_options = ["gpt-4", "gpt-4-32k", "gpt-4-1106-preview", "gpt-3.5-turbo", "gpt-3.5-turbo-16k"]
fetched_model_options = []
try:
fetched_model_options = get_model_options(
credentials=credentials, model_endpoint_type=model_endpoint_type, model_endpoint=model_endpoint
)
except Exception as e:
# NOTE: if this fails, it means the user's key is probably bad
typer.secho(
f"Failed to get model list from {model_endpoint} - make sure your API key and endpoints are correct!", fg=typer.colors.RED
)
raise e
# First ask if the user wants to see the full model list (some may be incompatible)
see_all_option_str = "[see all options]"
other_option_str = "[enter model name manually]"
# Check if the model we have set already is even in the list (informs our default)
valid_model = config.default_llm_config and config.default_llm_config.model in hardcoded_model_options
model = questionary.select(
"Select default model (recommended: gpt-4):",
choices=hardcoded_model_options + [see_all_option_str, other_option_str],
default=config.default_llm_config.model if valid_model else hardcoded_model_options[0],
).ask()
if model is None:
raise KeyboardInterrupt
# If the user asked for the full list, show it
if model == see_all_option_str:
typer.secho(f"Warning: not all models shown are guaranteed to work with MemGPT", fg=typer.colors.RED)
model = questionary.select(
"Select default model (recommended: gpt-4):",
choices=fetched_model_options + [other_option_str],
default=config.default_llm_config.model if (valid_model and config.default_llm_config) else fetched_model_options[0],
).ask()
if model is None:
raise KeyboardInterrupt
# Finally if the user asked to manually input, allow it
if model == other_option_str:
model = ""
while len(model) == 0:
model = questionary.text(
"Enter custom model name:",
).ask()
if model is None:
raise KeyboardInterrupt
elif model_endpoint_type == "google_ai":
try:
fetched_model_options = get_model_options(
credentials=credentials, model_endpoint_type=model_endpoint_type, model_endpoint=model_endpoint
)
except Exception as e:
# NOTE: if this fails, it means the user's key is probably bad
typer.secho(
f"Failed to get model list from {model_endpoint} - make sure your API key and endpoints are correct!", fg=typer.colors.RED
)
raise e
model = questionary.select(
"Select default model:",
choices=fetched_model_options,
default=fetched_model_options[0],
).ask()
if model is None:
raise KeyboardInterrupt
elif model_endpoint_type == "anthropic":
try:
fetched_model_options = get_model_options(
credentials=credentials, model_endpoint_type=model_endpoint_type, model_endpoint=model_endpoint
)
except Exception as e:
# NOTE: if this fails, it means the user's key is probably bad
typer.secho(
f"Failed to get model list from {model_endpoint} - make sure your API key and endpoints are correct!", fg=typer.colors.RED
)
raise e
model = questionary.select(
"Select default model:",
choices=fetched_model_options,
default=fetched_model_options[0],
).ask()
if model is None:
raise KeyboardInterrupt
elif model_endpoint_type == "cohere":
fetched_model_options = []
try:
fetched_model_options = get_model_options(
credentials=credentials, model_endpoint_type=model_endpoint_type, model_endpoint=model_endpoint
)
except Exception as e:
# NOTE: if this fails, it means the user's key is probably bad
typer.secho(
f"Failed to get model list from {model_endpoint} - make sure your API key and endpoints are correct!", fg=typer.colors.RED
)
raise e
fetched_model_options = [m["name"] for m in fetched_model_options]
hardcoded_model_options = [m for m in fetched_model_options if m in COHERE_VALID_MODEL_LIST]
# First ask if the user wants to see the full model list (some may be incompatible)
see_all_option_str = "[see all options]"
other_option_str = "[enter model name manually]"
# Check if the model we have set already is even in the list (informs our default)
valid_model = config.default_llm_config.model in hardcoded_model_options
model = questionary.select(
"Select default model (recommended: command-r-plus):",
choices=hardcoded_model_options + [see_all_option_str, other_option_str],
default=config.default_llm_config.model if valid_model else hardcoded_model_options[0],
).ask()
if model is None:
raise KeyboardInterrupt
# If the user asked for the full list, show it
if model == see_all_option_str:
typer.secho(f"Warning: not all models shown are guaranteed to work with MemGPT", fg=typer.colors.RED)
model = questionary.select(
"Select default model (recommended: command-r-plus):",
choices=fetched_model_options + [other_option_str],
default=config.default_llm_config.model if valid_model else fetched_model_options[0],
).ask()
if model is None:
raise KeyboardInterrupt
# Finally if the user asked to manually input, allow it
if model == other_option_str:
model = ""
while len(model) == 0:
model = questionary.text(
"Enter custom model name:",
).ask()
if model is None:
raise KeyboardInterrupt
else: # local models
# ask about local auth
if model_endpoint_type in ["groq"]: # TODO all llm engines under 'local' that will require api keys
use_local_auth = True
local_auth_type = "bearer_token"
local_auth_key = questionary.password(
"Enter your Groq API key:",
).ask()
if local_auth_key is None:
raise KeyboardInterrupt
credentials.openllm_auth_type = local_auth_type
credentials.openllm_key = local_auth_key
credentials.save()
else:
use_local_auth = questionary.confirm(
"Is your LLM endpoint authenticated? (default no)",
default=False,
).ask()
if use_local_auth is None:
raise KeyboardInterrupt
if use_local_auth:
local_auth_type = questionary.select(
"What HTTP authentication method does your endpoint require?",
choices=SUPPORTED_AUTH_TYPES,
default=SUPPORTED_AUTH_TYPES[0],
).ask()
if local_auth_type is None:
raise KeyboardInterrupt
local_auth_key = questionary.password(
"Enter your authentication key:",
).ask()
if local_auth_key is None:
raise KeyboardInterrupt
# credentials = MemGPTCredentials.load()
credentials.openllm_auth_type = local_auth_type
credentials.openllm_key = local_auth_key
credentials.save()
# ollama also needs model type
if model_endpoint_type == "ollama":
default_model = (
config.default_llm_config.model
if config.default_llm_config and config.default_llm_config.model_endpoint_type == "ollama"
else DEFAULT_OLLAMA_MODEL
)
model = questionary.text(
"Enter default model name (required for Ollama, see: https://memgpt.readme.io/docs/ollama):",
default=default_model,
).ask()
if model is None:
raise KeyboardInterrupt
model = None if len(model) == 0 else model
default_model = (
config.default_llm_config.model if config.default_llm_config and config.default_llm_config.model_endpoint_type == "vllm" else ""
)
# vllm needs huggingface model tag
if model_endpoint_type in ["vllm", "groq"]:
try:
# Don't filter model list for vLLM since model list is likely much smaller than OpenAI/Azure endpoint
# + probably has custom model names
# TODO support local auth
model_options = get_model_options(
credentials=credentials, model_endpoint_type=model_endpoint_type, model_endpoint=model_endpoint
)
except:
print(f"Failed to get model list from {model_endpoint}, using defaults")
model_options = None
# If we got model options from vLLM endpoint, allow selection + custom input
if model_options is not None:
other_option_str = "other (enter name)"
valid_model = config.default_llm_config.model in model_options
model_options.append(other_option_str)
model = questionary.select(
"Select default model:",
choices=model_options,
default=config.default_llm_config.model if valid_model else model_options[0],
).ask()
if model is None:
raise KeyboardInterrupt
# If we got custom input, ask for raw input
if model == other_option_str:
model = questionary.text(
"Enter HuggingFace model tag (e.g. ehartford/dolphin-2.2.1-mistral-7b):",
default=default_model,
).ask()
if model is None:
raise KeyboardInterrupt
# TODO allow empty string for input?
model = None if len(model) == 0 else model
else:
model = questionary.text(
"Enter HuggingFace model tag (e.g. ehartford/dolphin-2.2.1-mistral-7b):",
default=default_model,
).ask()
if model is None:
raise KeyboardInterrupt
model = None if len(model) == 0 else model
# model wrapper
available_model_wrappers = builtins.list(get_available_wrappers().keys())
model_wrapper = questionary.select(
f"Select default model wrapper (recommended: {DEFAULT_WRAPPER_NAME}):",
choices=available_model_wrappers,
default=DEFAULT_WRAPPER_NAME,
).ask()
if model_wrapper is None:
raise KeyboardInterrupt
# set: context_window
if str(model) not in LLM_MAX_TOKENS:
context_length_options = [
str(2**12), # 4096
str(2**13), # 8192
str(2**14), # 16384
str(2**15), # 32768
str(2**18), # 262144
"custom", # enter yourself
]
if model_endpoint_type == "google_ai":
try:
fetched_context_window = str(
google_ai_get_model_context_window(
service_endpoint=credentials.google_ai_service_endpoint, api_key=credentials.google_ai_key, model=model
)
)
print(f"Got context window {fetched_context_window} for model {model} (from Google API)")
context_length_options = [
fetched_context_window,
"custom",
]
except Exception as e:
print(f"Failed to get model details for model '{model}' on Google AI API ({str(e)})")
context_window_input = questionary.select(
"Select your model's context window (see https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versioning#gemini-model-versions):",
choices=context_length_options,
default=context_length_options[0],
).ask()
if context_window_input is None:
raise KeyboardInterrupt
elif model_endpoint_type == "anthropic":
try:
fetched_context_window = str(
antropic_get_model_context_window(url=model_endpoint, api_key=credentials.anthropic_key, model=model)
)
print(f"Got context window {fetched_context_window} for model {model}")
context_length_options = [
fetched_context_window,
"custom",
]
except Exception as e:
print(f"Failed to get model details for model '{model}' ({str(e)})")
context_window_input = questionary.select(
"Select your model's context window (see https://docs.anthropic.com/claude/docs/models-overview):",
choices=context_length_options,
default=context_length_options[0],
).ask()
if context_window_input is None:
raise KeyboardInterrupt
elif model_endpoint_type == "cohere":
try:
fetched_context_window = str(
cohere_get_model_context_window(url=model_endpoint, api_key=credentials.cohere_key, model=model)
)
print(f"Got context window {fetched_context_window} for model {model}")
context_length_options = [
fetched_context_window,
"custom",
]
except Exception as e:
print(f"Failed to get model details for model '{model}' ({str(e)})")
context_window_input = questionary.select(
"Select your model's context window (see https://docs.cohere.com/docs/command-r):",
choices=context_length_options,
default=context_length_options[0],
).ask()
if context_window_input is None:
raise KeyboardInterrupt
else:
# Ask the user to specify the context length
context_window_input = questionary.select(
"Select your model's context window (for Mistral 7B models, this is probably 8k / 8192):",
choices=context_length_options,
default=str(LLM_MAX_TOKENS["DEFAULT"]),
).ask()
if context_window_input is None:
raise KeyboardInterrupt
# If custom, ask for input
if context_window_input == "custom":
while True:
context_window_input = questionary.text("Enter context window (e.g. 8192)").ask()
if context_window_input is None:
raise KeyboardInterrupt
try:
context_window = int(context_window_input)
break
except ValueError:
print(f"Context window must be a valid integer")
else:
context_window = int(context_window_input)
else:
# Pull the context length from the models
context_window = int(LLM_MAX_TOKENS[str(model)])
return model, model_wrapper, context_window
def configure_embedding_endpoint(config: MemGPTConfig, credentials: MemGPTCredentials):
# configure embedding endpoint
default_embedding_endpoint_type = config.default_embedding_config.embedding_endpoint_type if config.default_embedding_config else None
embedding_endpoint_type, embedding_endpoint, embedding_dim, embedding_model = None, None, None, None
embedding_provider = questionary.select(
"Select embedding provider:", choices=["openai", "azure", "hugging-face", "local"], default=default_embedding_endpoint_type
).ask()
if embedding_provider is None:
raise KeyboardInterrupt
if embedding_provider == "openai":
# check for key
if credentials.openai_key is None:
# allow key to get pulled from env vars
openai_api_key = os.getenv("OPENAI_API_KEY", None)
if openai_api_key is None:
# if we still can't find it, ask for it as input
while openai_api_key is None or len(openai_api_key) == 0:
# Ask for API key as input
openai_api_key = questionary.password(
"Enter your OpenAI API key (starts with 'sk-', see https://platform.openai.com/api-keys):"
).ask()
if openai_api_key is None:
raise KeyboardInterrupt
credentials.openai_key = openai_api_key
credentials.save()
embedding_endpoint_type = "openai"
embedding_endpoint = "https://api.openai.com/v1"
embedding_dim = 1536
embedding_model = "text-embedding-ada-002"
elif embedding_provider == "azure":
# check for necessary vars
azure_creds = get_azure_credentials()
if not all([azure_creds["azure_key"], azure_creds["azure_embedding_endpoint"], azure_creds["azure_embedding_version"]]):
raise ValueError(
"Missing environment variables for Azure (see https://memgpt.readme.io/docs/endpoints#azure-openai). Please set then run `memgpt configure` again."
)
credentials.azure_key = azure_creds["azure_key"]
credentials.azure_version = azure_creds["azure_version"]
credentials.azure_embedding_endpoint = azure_creds["azure_embedding_endpoint"]
credentials.save()
embedding_endpoint_type = "azure"
embedding_endpoint = azure_creds["azure_embedding_endpoint"]
embedding_dim = 1536
embedding_model = "text-embedding-ada-002"
elif embedding_provider == "hugging-face":
# configure hugging face embedding endpoint (https://github.com/huggingface/text-embeddings-inference)
# supports custom model/endpoints
embedding_endpoint_type = "hugging-face"
embedding_endpoint = None
# get endpoint
embedding_endpoint = questionary.text("Enter default endpoint:").ask()
if embedding_endpoint is None:
raise KeyboardInterrupt
while not utils.is_valid_url(embedding_endpoint):
typer.secho(f"Endpoint must be a valid address", fg=typer.colors.YELLOW)
embedding_endpoint = questionary.text("Enter default endpoint:").ask()
if embedding_endpoint is None:
raise KeyboardInterrupt
# get model type
default_embedding_model = (
config.default_embedding_config.embedding_model if config.default_embedding_config else "BAAI/bge-large-en-v1.5"
)
embedding_model = questionary.text(
"Enter HuggingFace model tag (e.g. BAAI/bge-large-en-v1.5):",
default=default_embedding_model,
).ask()
if embedding_model is None:
raise KeyboardInterrupt
# get model dimentions
default_embedding_dim = config.default_embedding_config.embedding_dim if config.default_embedding_config else "1024"
embedding_dim = questionary.text("Enter embedding model dimentions (e.g. 1024):", default=str(default_embedding_dim)).ask()
if embedding_dim is None:
raise KeyboardInterrupt
try:
embedding_dim = int(embedding_dim)
except Exception:
raise ValueError(f"Failed to cast {embedding_dim} to integer.")
elif embedding_provider == "ollama":
# configure ollama embedding endpoint
embedding_endpoint_type = "ollama"
embedding_endpoint = "http://localhost:11434/api/embeddings"
# Source: https://github.com/ollama/ollama/blob/main/docs/api.md#generate-embeddings:~:text=http%3A//localhost%3A11434/api/embeddings
# get endpoint (is this necessary?)
embedding_endpoint = questionary.text("Enter Ollama API endpoint:").ask()
if embedding_endpoint is None:
raise KeyboardInterrupt
while not utils.is_valid_url(embedding_endpoint):
typer.secho(f"Endpoint must be a valid address", fg=typer.colors.YELLOW)
embedding_endpoint = questionary.text("Enter Ollama API endpoint:").ask()
if embedding_endpoint is None:
raise KeyboardInterrupt
# get model type
default_embedding_model = (
config.default_embedding_config.embedding_model if config.default_embedding_config else "mxbai-embed-large"
)
embedding_model = questionary.text(
"Enter Ollama model tag (e.g. mxbai-embed-large):",
default=default_embedding_model,
).ask()
if embedding_model is None:
raise KeyboardInterrupt
# get model dimensions
default_embedding_dim = config.default_embedding_config.embedding_dim if config.default_embedding_config else "512"
embedding_dim = questionary.text("Enter embedding model dimensions (e.g. 512):", default=str(default_embedding_dim)).ask()
if embedding_dim is None:
raise KeyboardInterrupt
try:
embedding_dim = int(embedding_dim)
except Exception:
raise ValueError(f"Failed to cast {embedding_dim} to integer.")
else: # local models
embedding_endpoint_type = "local"
embedding_endpoint = None
embedding_model = "BAAI/bge-small-en-v1.5"
embedding_dim = 384
return embedding_endpoint_type, embedding_endpoint, embedding_dim, embedding_model
def configure_archival_storage(config: MemGPTConfig, credentials: MemGPTCredentials):
# Configure archival storage backend
archival_storage_options = ["postgres", "chroma", "milvus", "qdrant"]
archival_storage_type = questionary.select(
"Select storage backend for archival data:", archival_storage_options, default=config.archival_storage_type
).ask()
if archival_storage_type is None:
raise KeyboardInterrupt
archival_storage_uri, archival_storage_path = config.archival_storage_uri, config.archival_storage_path
# configure postgres
if archival_storage_type == "postgres":
archival_storage_uri = questionary.text(
"Enter postgres connection string (e.g. postgresql+pg8000://{user}:{password}@{ip}:5432/{database}):",
default=config.archival_storage_uri if config.archival_storage_uri else "",
).ask()
if archival_storage_uri is None:
raise KeyboardInterrupt
# TODO: add back
## configure lancedb
# if archival_storage_type == "lancedb":
# archival_storage_uri = questionary.text(
# "Enter lanncedb connection string (e.g. ./.lancedb",
# default=config.archival_storage_uri if config.archival_storage_uri else "./.lancedb",
# ).ask()
# configure chroma
if archival_storage_type == "chroma":
chroma_type = questionary.select("Select chroma backend:", ["http", "persistent"], default="persistent").ask()
if chroma_type is None:
raise KeyboardInterrupt
if chroma_type == "http":
archival_storage_uri = questionary.text("Enter chroma ip (e.g. localhost:8000):", default="localhost:8000").ask()
if archival_storage_uri is None:
raise KeyboardInterrupt
if chroma_type == "persistent":
archival_storage_path = os.path.join(MEMGPT_DIR, "chroma")
if archival_storage_type == "qdrant":
qdrant_type = questionary.select("Select Qdrant backend:", ["local", "server"], default="local").ask()
if qdrant_type is None:
raise KeyboardInterrupt
if qdrant_type == "server":
archival_storage_uri = questionary.text(
"Enter the Qdrant instance URI (Default: localhost:6333):", default="localhost:6333"
).ask()
if archival_storage_uri is None:
raise KeyboardInterrupt
if qdrant_type == "local":
archival_storage_path = os.path.join(MEMGPT_DIR, "qdrant")
if archival_storage_type == "milvus":
default_milvus_uri = archival_storage_path = os.path.join(MEMGPT_DIR, "milvus.db")
archival_storage_uri = questionary.text(
f"Enter the Milvus connection URI (Default: {default_milvus_uri}):", default=default_milvus_uri
).ask()
if archival_storage_uri is None:
raise KeyboardInterrupt
return archival_storage_type, archival_storage_uri, archival_storage_path
# TODO: allow configuring embedding model
def configure_recall_storage(config: MemGPTConfig, credentials: MemGPTCredentials):
# Configure recall storage backend
recall_storage_options = ["sqlite", "postgres"]
recall_storage_type = questionary.select(
"Select storage backend for recall data:", recall_storage_options, default=config.recall_storage_type
).ask()
if recall_storage_type is None:
raise KeyboardInterrupt
recall_storage_uri, recall_storage_path = config.recall_storage_uri, config.recall_storage_path
# configure postgres
if recall_storage_type == "postgres":
recall_storage_uri = questionary.text(
"Enter postgres connection string (e.g. postgresql+pg8000://{user}:{password}@{ip}:5432/{database}):",
default=config.recall_storage_uri if config.recall_storage_uri else "",
).ask()
if recall_storage_uri is None:
raise KeyboardInterrupt
return recall_storage_type, recall_storage_uri, recall_storage_path
@app.command()
def configure():
"""Updates default MemGPT configurations
This function and quickstart should be the ONLY place where MemGPTConfig.save() is called