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View Code? Open in Web Editor NEWImplementation of "Generative Agents: Interactive Simulacra of Human Behavior" paper with Guidance and Langchain. Full features and work with local LLMs.
License: Other
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jjhw dkzdev brandonmcclure githubzuoyi snoopycn maximegmd cuizhenkai githungdang softwareengineer-imerjr seanstar jiaoyining benwaldner c00renut zhengbaibai acadeum7 jamesthesnake jprussoibanez azure-arc-0 apollohuang1 cat-l jfontestad j4yh3m syaikhipin vital121 walchshofer sinianyutian schroederdewitt nutboltz trawmoney byteshow1234 sorokinvld v-straussgenerativeagent_llm's Issues
BaseRetriever.get_relevant_documents() takes 2 positional arguments but 3 were given
sam_observations = [
"Sam wake up in the morning",
]
sam.add_memories(sam_observations)
summary = sam.get_summary(force_refresh=True)
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ in <cell line: 1>:1 │
│ │
│ /content/generativeAgent_LLM/server/generativeAgent.py:163 in get_summary │
│ │
│ 160 │ │ │ or since_refresh >= self.summary_refresh_seconds │
│ 161 │ │ │ or force_refresh │
│ 162 │ │ ): │
│ ❱ 163 │ │ │ core_characteristics = self._run_characteristics() │
│ 164 │ │ │ daily_occupation = self._run_occupation() │
│ 165 │ │ │ feeling = self._run_feeling() │
│ 166 │
│ │
│ /content/generativeAgent_LLM/server/generativeAgent.py:173 in _run_characteristics │
│ │
│ 170 │ │ return self.summary │
│ 171 │ │
│ 172 │ def _run_characteristics(self,): │
│ ❱ 173 │ │ docs = self.retriever.get_relevant_documents(self.name + "'s core characteristic │
│ 174 │ │ statements = get_text_from_docs(docs, include_time = False) │
│ 175 │ │ │
│ 176 │ │ prompt = self.guidance(PROMPT_CHARACTERISTICS, silent=self.silent) │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
TypeError: BaseRetriever.get_relevant_documents() takes 2 positional arguments but 3 were given
Update
I can get around this by making def get_relevant_documents(self, query: str, current_time=None)
and invoking
with mock_now(datetime.datetime(2011, 2, 3, 10, 11)):
print(sam.retriever.get_relevant_documents("sam's core characteristics")
[Document(page_content='Sam is a Ph.D student, his major is CS', metadata={'importance': 7, 'created_at': datetime.datetime(2023, 7, 19, 7, 25, 42, 649173), 'last_accessed_at': MockDateTime(2011, 2, 3, 10, 11), 'buffer_idx': 0}), Document(page_content='Sam likes computer', metadata={'importance': 1, 'created_at': datetime.datetime(2023, 7, 19, 7, 25, 42, 649173), 'last_accessed_at': MockDateTime(2011, 2, 3, 10, 11), 'buffer_idx': 1}), Document(page_content='Wants to get his AI company to be successful.', metadata={'importance': 8, 'created_at': datetime.datetime(2023, 7, 19, 7, 25, 42, 649173), 'last_accessed_at': MockDateTime(2011, 2, 3, 10, 11), 'buffer_idx': 5}), Document(page_content='Sam really wants to get to work to get the marketing project for his AI company done', metadata={'importance': 3, 'created_at': datetime.datetime(2023, 7, 19, 7, 25, 42, 649173), 'last_accessed_at': MockDateTime(2011, 2, 3, 10, 11), 'buffer_idx': 7}), Document(page_content='Sam wake up in the morning', metadata={'importance': 1, 'created_at': datetime.datetime(2023, 7, 19, 7, 25, 42, 649173), 'last_accessed_at': MockDateTime(2011, 2, 3, 10, 11), 'buffer_idx': 6}), Document(page_content='Sam has a dog, named Max', metadata={'importance': 1, 'created_at': datetime.datetime(2023, 7, 19, 7, 25, 42, 649173), 'last_accessed_at': MockDateTime(2011, 2, 3, 10, 11), 'buffer_idx': 4}), Document(page_content="Sam's farther is a doctor", metadata={'importance': 1, 'created_at': datetime.datetime(2023, 7, 19, 7, 25, 42, 649173), 'last_accessed_at': MockDateTime(2011, 2, 3, 10, 11), 'buffer_idx': 3}), Document(page_content='Sam lives with his friend, Bob', metadata={'importance': 1, 'created_at': datetime.datetime(2023, 7, 19, 7, 25, 42, 649173), 'last_accessed_at': MockDateTime(2011, 2, 3, 10, 11), 'buffer_idx': 2})]
I really don't understand the issue - is it the timestamp passed?
Why the score add "1-vertor_relevance"
The method _get_combined_score_list in TimeWeightedVectorStoreRetrieverModified class has this:
generativeAgent_LLM/server/time_weighted_retriever.py
Lines 91 to 93 in 17d67f6
The original implementation in Langchain adds vector_relevance but why do you add 1-vertor_relevance here
Share a requirement.txt
Hi, can someone share requirements.txt , env.yaml /toml, or and sort of environment setup file for cuda 12.1. I have access to a cloud server but unable to change the cuda version.
How could i run this code with openai api, The OpenAI API does not support Guidance pattern controls.
Problem in loading models
model=TheBloke_Llama-2-13B-GPTQ/model.safetensors, I also tried: Wizard-Vicuna-7B-Uncensored-GPTQ-4bit-128g.no-act-order.safetensors, same problem
Loading model ...
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In[36], line 5
1 # MODEL_PATH = '/home/quang/working/LLMs/oobabooga_linux/text-generation-webui/models/TheBloke_Wizard-Vicuna-7B-Uncensored-GPTQ'
2 # CHECKPOINT_PATH = '/home/quang/working/LLMs/oobabooga_linux/text-generation-webui/models/TheBloke_Wizard-Vicuna-7B-Uncensored-GPTQ/Wizard-Vicuna-7B-Uncensored-GPTQ-4bit-128g.no-act-order.safetensors'
4 DEVICE = torch.device('cuda:0')
----> 5 model, tokenizer = load_model_main(MODEL_PATH, CHECKPOINT_PATH, DEVICE)
File /mnt/data/generativeAgent_LLM/server/model.py:56, in load_model_main(model_para, checkpoint_para, device)
55 def load_model_main(model_para, checkpoint_para, device):
---> 56 model = load_quant(model_para, checkpoint_para, 4, 128)
57 model.to(device)
58 tokenizer = AutoTokenizer.from_pretrained(model_para)
File /mnt/data/generativeAgent_LLM/server/model.py:47, in load_quant(model, checkpoint, wbits, groupsize)
45 if checkpoint.endswith('.safetensors'):
46 from safetensors.torch import load_file as safe_load
---> 47 model.load_state_dict(safe_load(checkpoint))
48 else:
49 model.load_state_dict(torch.load(checkpoint))
File ~/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py:1671, in Module.load_state_dict(self, state_dict, strict)
1666 error_msgs.insert(
1667 0, 'Missing key(s) in state_dict: {}. '.format(
1668 ', '.join('"{}"'.format(k) for k in missing_keys)))
1670 if len(error_msgs) > 0:
-> 1671 raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
1672 self.__class__.__name__, "\n\t".join(error_msgs)))
1673 return _IncompatibleKeys(missing_keys, unexpected_keys)
RuntimeError: Error(s) in loading state_dict for LlamaForCausalLM:
Unexpected key(s) in state_dict: "model.layers.0.self_attn.rotary_emb.inv_freq", "model.layers.0.self_attn.k_proj.g_idx", "model.layers.0.self_attn.o_proj.g_idx", "model.layers.0.self_attn.q_proj.g_idx", "model.layers.0.self_attn.v_proj.g_idx", "model.layers.0.mlp.down_proj.g_idx", "model.layers.0.mlp.gate_proj.g_idx", "model.layers.0.mlp.up_proj.g_idx", "model.layers.1.self_attn.rotary_emb.inv_freq", "model.layers.1.self_attn.k_proj.g_idx", "model.layers.1.self_attn.o_proj.g_idx", "model.layers.1.self_attn.q_proj.g_idx", "model.layers.1.self_attn.v_proj.g_idx", "model.layers.1.mlp.down_proj.g_idx", "model.layers.1.mlp.gate_proj.g_idx", "model.layers.1.mlp.up_proj.g_idx", "model.layers.2.self_attn.rotary_emb.inv_freq", "model.layers.2.self_attn.k_proj.g_idx", "model.layers.2.self_attn.o_proj.g_idx", "model.layers.2.self_attn.q_proj.g_idx", "model.layers.2.self_attn.v_proj.g_idx", "model.layers.2.mlp.down_proj.g_idx", "model.layers.2.mlp.gate_proj.g_idx",
keyerror items when adding memories
### Instruction:
{{recent_memories}}
### Input:
Given only the information above, what are 3 most salient high-level questions we can answer about the subjects in the statements?
### Response:
{{#geneach 'items' num_iterations=3}}{{gen 'this' top_k=30 top_p=0.18 repetition_penalty=1.15 temperature=1.99 stop='
'}}
{{/geneach}}
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ in <cell line: 6>:6 │
│ │
│ /content/generativeAgent_LLM/server/generativeAgent.py:122 in add_memories │
│ │
│ 119 │ │ │
│ 120 │ │ if not self.reflecting and self.aggregate_importance > self.reflection_threshold │
│ 121 │ │ │ self.reflecting = True │
│ ❱ 122 │ │ │ self._relection() │
│ 123 │ │ │ self.aggregate_importance = 0.0 │
│ 124 │ │ │ self.reflecting = False │
│ 125 │
│ │
│ /content/generativeAgent_LLM/server/generativeAgent.py:144 in _relection │
│ │
│ 141 │ │ return result['items'] │
│ 142 │ │
│ 143 │ def _relection(self,): │
│ ❱ 144 │ │ list_salient = self._get_salient() │
│ 145 │ │ list_docs = [] │
│ 146 │ │ for salient_temp in list_salient: │
│ 147 │ │ │ docs = self.retriever.get_relevant_documents(salient_temp, self.get_current_ │
│ │
│ /content/generativeAgent_LLM/server/generativeAgent.py:134 in _get_salient │
│ │
│ 131 │ │ │
│ 132 │ │ prompt = self.guidance(PROMPT_SALIENT, silent=self.silent) │
│ 133 │ │ result = prompt(recent_memories=recent_memories_text) │
│ ❱ 134 │ │ return result['items'] │
│ 135 │ │
│ 136 │ def _get_insights(self, list_docs): │
│ 137 │ │ docs = list_docs │
│ │
│ /usr/local/lib/python3.10/dist-packages/guidance/_program.py:470 in __getitem__ │
│ │
│ 467 │ │ │ self._execute_complete.set() │
│ 468 │ │
│ 469 │ def __getitem__(self, key): │
│ ❱ 470 │ │ return self._variables[key] │
│ 471 │ │
│ 472 │ def __contains__(self, key): │
│ 473 │ │ return key in self._variables │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
KeyError: 'items'
result is actually this string
### Instruction:
{{recent_memories}}
### Input:
Given only the information above, what are 3 most salient high-level questions we can answer about the subjects in the statements?
### Response:
{{#geneach 'items' num_iterations=3}}{{gen 'this' top_k=30 top_p=0.18 repetition_penalty=1.15 temperature=1.99 stop='
'}}
{{/geneach}}
update
adding a single memory seems to work
Loop
Did you start working on the idea of the "simulation loop"? What I don't get from the paper (where they also have spatial movement): is there a world-state including a world tick, or how would the loop look like, do agents decide what to do next (reflect, obsever, ...) or is this a pre-programmed script when implemented.
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