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refactor: remove User LLM/embed. defaults, add credentials file, add authentication option for custom LLM backends (#835)

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This commit is contained in:
Charles Packer
2024-01-18 16:11:35 -08:00
committed by GitHub
parent 2f7ccb1807
commit da5a8cdbfe
27 changed files with 454 additions and 513 deletions
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41
tests/test_server.py
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@@ -4,6 +4,7 @@ import memgpt.utils as utils
utils.DEBUG = True
from memgpt.config import MemGPTConfig
from memgpt.credentials import MemGPTCredentials
from memgpt.server.server import SyncServer
from memgpt.data_types import EmbeddingConfig, AgentState, LLMConfig, Message, Passage, User
from memgpt.embeddings import embedding_model
@@ -22,14 +23,20 @@ def test_server():
recall_storage_type="postgres",
metadata_storage_type="postgres",
# embeddings
embedding_endpoint_type="openai",
embedding_endpoint="https://api.openai.com/v1",
embedding_dim=1536,
openai_key=os.getenv("OPENAI_API_KEY"),
default_embedding_config=EmbeddingConfig(
embedding_endpoint_type="openai",
embedding_endpoint="https://api.openai.com/v1",
embedding_dim=1536,
),
# llms
model_endpoint_type="openai",
model_endpoint="https://api.openai.com/v1",
model="gpt-4",
default_llm_config=LLMConfig(
model_endpoint_type="openai",
model_endpoint="https://api.openai.com/v1",
model="gpt-4",
),
)
credentials = MemGPTCredentials(
openai_key=os.getenv("OPENAI_API_KEY"),
)
else: # hosted
config = MemGPTConfig(
@@ -40,16 +47,22 @@ def test_server():
recall_storage_type="postgres",
metadata_storage_type="postgres",
# embeddings
embedding_endpoint_type="hugging-face",
embedding_endpoint="https://embeddings.memgpt.ai",
embedding_model="BAAI/bge-large-en-v1.5",
embedding_dim=1024,
default_embedding_config=EmbeddingConfig(
embedding_endpoint_type="hugging-face",
embedding_endpoint="https://embeddings.memgpt.ai",
embedding_model="BAAI/bge-large-en-v1.5",
embedding_dim=1024,
),
# llms
model_endpoint_type="vllm",
model_endpoint="https://api.memgpt.ai",
model="ehartford/dolphin-2.5-mixtral-8x7b",
default_llm_config=LLMConfig(
model_endpoint_type="vllm",
model_endpoint="https://api.memgpt.ai",
model="ehartford/dolphin-2.5-mixtral-8x7b",
),
)
credentials = MemGPTCredentials()
config.save()
credentials.save()
server = SyncServer()