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Created new agent agent_1. Available functions: ['send_message', 'pause_heartbeats', 'core_memory_append', 'core_memory_replace', 'conversation_search', 'conversation_search_date', 'archival_memory_insert', 'archival_memory_search'] AgentAsync initialized, self.messages_total=3 Initializing InMemoryStateManager with agent object InMemoryStateManager.all_messages.len = 4 InMemoryStateManager.messages.len = 4 Hit enter to begin (will request first MemGPT message) This is the first message. Running extra verifier on AI response. 💭 Persona activated for user cs_phd. Greetings and welcome, how may I assist you today? 🤖 Greetings and welcome, how may I assist you today? ⚡🟢 [function] Success: None last response total_tokens (0) < 7000 InMemoryStateManager.append_to_messages > Enter your message: hi 🧑 {'message': 'hi', 'time': '2023-10-31 08:56:50 AM PDT-0700'} 💭 Beginning conversation with user cs_phd. Preparing opening greeting... 🤖 Hello there! ⚡🟢 [function] Success: None last response total_tokens (0) < 7000 InMemoryStateManager.append_to_messages > Enter your message: /save > Enter your message: /memory Dumping memory contents: ### CORE MEMORY ### === Persona === sam === Human === cs_phd ╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮ │ /home/john/MemGPT/memgpt/cli/cli.py:148 in run │ │ │ │ 145 │ from memgpt.main import run_agent_loop │ │ 146 │ │ │ 147 │ loop = asyncio.get_event_loop() │ │ ❱ 148 │ loop.run_until_complete(run_agent_loop(memgpt_agent, first, no_verify, config)) # T │ │ 149 │ │ │ │ ╭─────────────────────────────────────────── locals ───────────────────────────────────────────╮ │ │ │ agent = None │ │ │ │ agent_config = AgentConfig() │ │ │ │ agent_files = [] │ │ │ │ agents = [] │ │ │ │ config = MemGPTConfig( │ │ │ │ │ config_path='/home/john/.memgpt/config', │ │ │ │ │ anon_clientid='00000000000000000000f6aecac3218d', │ │ │ │ │ preset='memgpt_chat', │ │ │ │ │ model_endpoint='http://192.168.0.109:9090', │ │ │ │ │ model='gpt-4', │ │ │ │ │ openai_key='test', │ │ │ │ │ azure_key=None, │ │ │ │ │ azure_endpoint=None, │ │ │ │ │ azure_version=None, │ │ │ │ │ azure_deployment=None, │ │ │ │ │ azure_embedding_deployment=None, │ │ │ │ │ default_persona='sam', │ │ │ │ │ default_human='cs_phd', │ │ │ │ │ default_agent=None, │ │ │ │ │ embedding_model='openai', │ │ │ │ │ embedding_dim=768, │ │ │ │ │ embedding_chunk_size=300, │ │ │ │ │ archival_storage_type='local', │ │ │ │ │ archival_storage_path=None, │ │ │ │ │ archival_storage_uri=None, │ │ │ │ │ recall_storage_type='local', │ │ │ │ │ recall_storage_path=None, │ │ │ │ │ recall_storage_uri=None, │ │ │ │ │ persistence_manager_type=None, │ │ │ │ │ persistence_manager_save_file=None, │ │ │ │ │ persistence_manager_uri=None │ │ │ │ ) │ │ │ │ data_source = None │ │ │ │ debug = True │ │ │ │ embed_model = HuggingFaceEmbedding( │ │ │ │ │ model_name='BAAI/bge-small-en-v1.5', │ │ │ │ │ embed_batch_size=10, │ │ │ │ │ callback_manager=<llama_index.callbacks.base.CallbackManager │ │ │ │ object at 0x7fc32035e4d0>, │ │ │ │ │ tokenizer_name='BAAI/bge-small-en-v1.5', │ │ │ │ │ max_length=512, │ │ │ │ │ pooling=<Pooling.CLS: 'cls'>, │ │ │ │ │ normalize='True', │ │ │ │ │ query_instruction=None, │ │ │ │ │ text_instruction=None, │ │ │ │ │ cache_folder=None │ │ │ │ ) │ │ │ │ first = False │ │ │ │ human = None │ │ │ │ loop = <_UnixSelectorEventLoop running=False closed=False debug=False> │ │ │ │ memgpt_agent = <memgpt.agent.AgentAsync object at 0x7fc31f6ae550> │ │ │ │ model = 'airoboros-l2-70b-2.1' │ │ │ │ no_verify = False │ │ │ │ original_stdout = <colorama.ansitowin32.StreamWrapper object at 0x7fc3f389e250> │ │ │ │ persistence_manager = <memgpt.persistence_manager.LocalStateManager object at │ │ │ │ 0x7fc317b67a90> │ │ │ │ persona = None │ │ │ │ preset = None │ │ │ │ run_agent_loop = <function run_agent_loop at 0x7fc3cfdae5c0> │ │ │ │ service_context = ServiceContext( │ │ │ │ │ llm_predictor=LLMPredictor( │ │ │ │ │ │ system_prompt=None, │ │ │ │ │ │ query_wrapper_prompt=None, │ │ │ │ │ │ pydantic_program_mode=<PydanticProgramMode.DEFAULT: 'default'> │ │ │ │ │ ), │ │ │ │ │ prompt_helper=PromptHelper( │ │ │ │ │ │ context_window=3900, │ │ │ │ │ │ num_output=256, │ │ │ │ │ │ chunk_overlap_ratio=0.1, │ │ │ │ │ │ chunk_size_limit=None, │ │ │ │ │ │ separator=' ' │ │ │ │ │ ), │ │ │ │ │ embed_model=HuggingFaceEmbedding( │ │ │ │ │ │ model_name='BAAI/bge-small-en-v1.5', │ │ │ │ │ │ embed_batch_size=10, │ │ │ │ │ │ callback_manager=<llama_index.callbacks.base.CallbackManager │ │ │ │ object at 0x7fc32035e4d0>, │ │ │ │ │ │ tokenizer_name='BAAI/bge-small-en-v1.5', │ │ │ │ │ │ max_length=512, │ │ │ │ │ │ pooling=<Pooling.CLS: 'cls'>, │ │ │ │ │ │ normalize='True', │ │ │ │ │ │ query_instruction=None, │ │ │ │ │ │ text_instruction=None, │ │ │ │ │ │ cache_folder=None │ │ │ │ │ ), │ │ │ │ │ node_parser=SimpleNodeParser( │ │ │ │ │ │ text_splitter=SentenceSplitter( │ │ │ │ │ │ │ chunk_size=300, │ │ │ │ │ │ │ chunk_overlap=20, │ │ │ │ │ │ │ separator=' ', │ │ │ │ │ │ │ paragraph_separator='\n\n\n', │ │ │ │ │ │ │ secondary_chunking_regex='[^,.;。?!]+[,.;。?!]?', │ │ │ │ │ │ │ chunking_tokenizer_fn=<function │ │ │ │ split_by_sentence_tokenizer.<locals>.split at 0x7fc31f549800>, │ │ │ │ │ │ │ │ │ │ │ callback_manager=<llama_index.callbacks.base.CallbackManager object at │ │ │ │ 0x7fc32035e4d0>, │ │ │ │ │ │ │ tokenizer=functools.partial(<bound method Encoding.encode │ │ │ │ of <Encoding 'gpt2'>>, allowed_special='all') │ │ │ │ │ │ ), │ │ │ │ │ │ include_metadata=True, │ │ │ │ │ │ include_prev_next_rel=True, │ │ │ │ │ │ metadata_extractor=None, │ │ │ │ │ │ callback_manager=<llama_index.callbacks.base.CallbackManager │ │ │ │ object at 0x7fc32035e4d0> │ │ │ │ │ ), │ │ │ │ │ llama_logger=<llama_index.logger.base.LlamaLogger object at │ │ │ │ 0x7fc31f642290>, │ │ │ │ │ callback_manager=<llama_index.callbacks.base.CallbackManager │ │ │ │ object at 0x7fc32035e4d0> │ │ │ │ ) │ │ │ │ yes = False │ │ │ ╰──────────────────────────────────────────────────────────────────────────────────────────────╯ │ │ │ │ /home/john/miniconda3/envs/memgpt/lib/python3.11/asyncio/base_events.py:653 in │ │ run_until_complete │ │ │ │ 650 │ │ if not future.done(): │ │ 651 │ │ │ raise RuntimeError('Event loop stopped before Future completed.') │ │ 652 │ │ │ │ ❱ 653 │ │ return future.result() │ │ 654 │ │ │ 655 │ def stop(self): │ │ 656 │ │ """Stop running the event loop. │ │ │ │ ╭─────────────────────────────────────────── locals ───────────────────────────────────────────╮ │ │ │ future = <Task finished name='Task-1' coro=<run_agent_loop() done, defined at │ │ │ │ /home/john/MemGPT/memgpt/main.py:361> │ │ │ │ exception=NotImplementedError('ref_doc_info not supported for an empty index.')> │ │ │ │ new_task = True │ │ │ │ self = <_UnixSelectorEventLoop running=False closed=False debug=False> │ │ │ ╰──────────────────────────────────────────────────────────────────────────────────────────────╯ │ │ │ │ /home/john/MemGPT/memgpt/main.py:461 in run_agent_loop │ │ │ │ 458 │ │ │ │ elif user_input.lower() == "/memory": │ │ 459 │ │ │ │ │ print(f"\nDumping memory contents:\n") │ │ 460 │ │ │ │ │ print(f"{str(memgpt_agent.memory)}") │ │ ❱ 461 │ │ │ │ │ print(f"{str(memgpt_agent.persistence_manager.archival_memory)}") │ │ 462 │ │ │ │ │ print(f"{str(memgpt_agent.persistence_manager.recall_memory)}") │ │ 463 │ │ │ │ │ continue │ │ 464 │ │ │ │ ╭─────────────────────────────────────────── locals ───────────────────────────────────────────╮ │ │ │ cfg = MemGPTConfig( │ │ │ │ │ config_path='/home/john/.memgpt/config', │ │ │ │ │ anon_clientid='00000000000000000000f6aecac3218d', │ │ │ │ │ preset='memgpt_chat', │ │ │ │ │ model_endpoint='http://192.168.0.109:9090', │ │ │ │ │ model='gpt-4', │ │ │ │ │ openai_key='test', │ │ │ │ │ azure_key=None, │ │ │ │ │ azure_endpoint=None, │ │ │ │ │ azure_version=None, │ │ │ │ │ azure_deployment=None, │ │ │ │ │ azure_embedding_deployment=None, │ │ │ │ │ default_persona='sam', │ │ │ │ │ default_human='cs_phd', │ │ │ │ │ default_agent=None, │ │ │ │ │ embedding_model='openai', │ │ │ │ │ embedding_dim=768, │ │ │ │ │ embedding_chunk_size=300, │ │ │ │ │ archival_storage_type='local', │ │ │ │ │ archival_storage_path=None, │ │ │ │ │ archival_storage_uri=None, │ │ │ │ │ recall_storage_type='local', │ │ │ │ │ recall_storage_path=None, │ │ │ │ │ recall_storage_uri=None, │ │ │ │ │ persistence_manager_type=None, │ │ │ │ │ persistence_manager_save_file=None, │ │ │ │ │ persistence_manager_uri=None │ │ │ │ ) │ │ │ │ counter = 2 │ │ │ │ first = False │ │ │ │ function_failed = False │ │ │ │ heartbeat_request = None │ │ │ │ legacy = False │ │ │ │ memgpt_agent = <memgpt.agent.AgentAsync object at 0x7fc31f6ae550> │ │ │ │ multiline_input = False │ │ │ │ new_messages = [ │ │ │ │ │ { │ │ │ │ │ │ 'role': 'user', │ │ │ │ │ │ 'content': '{"type": "user_message", "message": "hi", "time": │ │ │ │ "2023-10-31 08:56:50 AM PDT-07'+4 │ │ │ │ │ }, │ │ │ │ │ { │ │ │ │ │ │ 'role': 'assistant', │ │ │ │ │ │ 'content': 'Beginning conversation with user cs_phd. │ │ │ │ Preparing opening greeting... ', │ │ │ │ │ │ 'function_call': { │ │ │ │ │ │ │ 'name': 'send_message', │ │ │ │ │ │ │ 'arguments': '{"message": "Hello there!"}' │ │ │ │ │ │ } │ │ │ │ │ }, │ │ │ │ │ { │ │ │ │ │ │ 'role': 'function', │ │ │ │ │ │ 'name': 'send_message', │ │ │ │ │ │ 'content': '{"status": "OK", "message": null, "time": │ │ │ │ "2023-10-31 08:56:56 AM PDT-0700"}' │ │ │ │ │ } │ │ │ │ ] │ │ │ │ no_verify = False │ │ │ │ skip_next_user_input = False │ │ │ │ status = <rich.status.Status object at 0x7fc317ad7fd0> │ │ │ │ token_warning = False │ │ │ │ USER_GOES_FIRST = False │ │ │ │ user_input = '/memory' │ │ │ │ user_message = '{"type": "user_message", "message": "hi", "time": "2023-10-31 │ │ │ │ 08:56:50 AM PDT-07'+4 │ │ │ ╰──────────────────────────────────────────────────────────────────────────────────────────────╯ │ │ │ │ /home/john/MemGPT/memgpt/memory.py:725 in __repr__ │ │ │ │ 722 │ │ return self.search(query_string, count, start) │ │ 723 │ │ │ 724 │ def __repr__(self) -> str: │ │ ❱ 725 │ │ print(self.index.ref_doc_info) │ │ 726 │ │ return "" │ │ 727 │ │ │ │ ╭─────────────────────────────── locals ───────────────────────────────╮ │ │ │ self = <repr-error 'ref_doc_info not supported for an empty index.'> │ │ │ ╰──────────────────────────────────────────────────────────────────────╯ │ │ │ │ /home/john/miniconda3/envs/memgpt/lib/python3.11/site-packages/llama_index/indices/empty/base.p │ │ y:85 in ref_doc_info │ │ │ │ 82 │ @property │ │ 83 │ def ref_doc_info(self) -> Dict[str, RefDocInfo]: │ │ 84 │ │ """Retrieve a dict mapping of ingested documents and their nodes+metadata.""" │ │ ❱ 85 │ │ raise NotImplementedError("ref_doc_info not supported for an empty index.") │ │ 86 │ │ 87 │ │ 88 # legacy │ │ │ │ ╭────────────────────────────────── locals ───────────────────────────────────╮ │ │ │ self = <llama_index.indices.empty.base.EmptyIndex object at 0x7fc31704a810> │ │ │ ╰─────────────────────────────────────────────────────────────────────────────╯ │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ NotImplementedError: ref_doc_info not supported for an empty index.```
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@sarahwooders llama index bug?
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Addressed by #240
sarahwooders
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The text was updated successfully, but these errors were encountered: