updated requirements because I had installed this in WSL2
This commit is contained in:
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@ -50,4 +50,6 @@ Even using one that's more instruction-tuned like Vicuna (with a `SYSTEM:\nUSER:
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However, I seem to be getting consistent results with SuperCOT 33B, it's just, well, slow. SuperCOT 13B seems to be giving better answers over Vicuna-1.1 13B, so.
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A ***lot*** of prompt wrangling is needed, and a lot of the routines could be polished up (for example, an observation queries the LM for a rating, and each response reaction requires quering for the observed entity, then the relationship between an agent and observed entity which ends up just summarizing relevant context/memories, and then queries for a response), and if one of these steps fails, then the fail rate is higher. If anything, I might as well just work from the ground up and only really salvage the use of FAISS to store embedded-vectors.
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GPT4 seems to Just Work, unfortunately.
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@ -1,4 +1,6 @@
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langchain
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openai
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llamacpp
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llama-cpp-python
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gradio
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faiss-cpu
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termcolor
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@ -34,7 +34,7 @@ from langchain.prompts import PromptTemplate
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from langchain.schema import BaseLanguageModel
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from .memory import GenerativeAgentMemory
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from .prompts import get_prompt
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from .prompts import get_prompt, get_stop_tokens
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class GenerativeAgent(BaseModel):
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"""A character with memory and innate characteristics."""
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@ -87,14 +87,14 @@ class GenerativeAgent(BaseModel):
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def _get_entity_from_observation(self, observation: str) -> str:
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prompt = PromptTemplate.from_template(get_prompt('entity_from_observation'))
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response = self.chain(prompt).run(observation=observation).strip().replace("Entity=", "").replace("Entity: ", "") # OAI will keep this
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response = self.chain(prompt).run(stop=get_stop_tokens([".", "(", "'"]), observation=observation).strip()
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if self.verbose:
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print(response)
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return response
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def _get_entity_action(self, observation: str, entity_name: str) -> str:
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prompt = PromptTemplate.from_template(get_prompt('entity_action'))
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response = self.chain(prompt).run(entity=entity_name, observation=observation).strip()
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response = self.chain(prompt).run(stop=get_stop_tokens(), entity=entity_name, observation=observation).strip()
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if self.verbose:
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print(response)
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return response
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@ -113,21 +113,23 @@ class GenerativeAgent(BaseModel):
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entity_action = self._get_entity_action(observation, entity_name)
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q2 = f"{entity_name} is {entity_action}"
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summary = self.chain(prompt=prompt).run(q1=q1, queries=[q1, q2]).strip()
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return summary
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summary = self.chain(prompt=prompt).run(name=self.name, stop=get_stop_tokens(), q1=q1, queries=[q1, q2]).strip()
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return f'{self.name} {summary}'
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#return self.chain(prompt=prompt).run(q1=q1, q2=q2).strip()
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#return self.chain(prompt=prompt).run(stop=get_stop_tokens(), q1=q1, q2=q2).strip()
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def _generate_reaction(self, observation: str, suffix: str) -> str:
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"""React to a given observation or dialogue act."""
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prompt = PromptTemplate.from_template(
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get_prompt('generate_reaction').replace("{suffix}", suffix)
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)
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summary = self.get_summary()
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relevant_memories = self.summarize_related_memories(observation)
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summary = self.get_summary().replace(u"\u200B", "").strip()
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relevant_memories = self.summarize_related_memories(observation).replace(u"\u200B", "").strip()
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recent_memories = "\n".join(self.get_most_recent_memories())
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# I think relevant_memories is suppose to only provide context for a relationship between agent and observer, as suggested with the query
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# but the original implementation seems to just leverage it to further filter relevant memories, per the name
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if relevant_memories and relevant_memories != "N/A":
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memory = relevant_memories
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else:
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@ -140,9 +142,11 @@ class GenerativeAgent(BaseModel):
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status=self.status if self.status else "N/A",
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summary=summary if summary else "N/A",
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memory=memory if memory else "N/A",
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#relevant_memories=relevant_memories if relevant_memories else "N/A",
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#recent_memories=recent_memories if recent_memories else "N/A",
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observation=observation if observation else "N/A",
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)
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reaction = self.chain(prompt=prompt).run(**kwargs).strip()
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reaction = self.chain(prompt=prompt).run(stop=get_stop_tokens(), **kwargs).strip()
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if self.verbose:
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print(reaction)
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return reaction
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@ -150,6 +154,20 @@ class GenerativeAgent(BaseModel):
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def _clean_response(self, text: str) -> str:
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return re.sub(f"^{self.name} ", "", text.strip()).strip()
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def generate_response(self, observation: str) -> Tuple[bool, str]:
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"""React to a given observation."""
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call_to_action_template = get_prompt('suffix_generate_response')
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full_result = f"{self.name} {self._generate_reaction(observation, call_to_action_template)}"
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self.memory.save_context(
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{},
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{
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self.memory.add_memory_key: full_result
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},
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)
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return True, full_result
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def generate_reaction(self, observation: str) -> Tuple[bool, str]:
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"""React to a given observation."""
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full_result = self._generate_reaction(observation, get_prompt('suffix_generate_reaction'))
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@ -191,9 +209,9 @@ class GenerativeAgent(BaseModel):
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return False, f"{self.name} did not react in a relevant way"
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"""
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def generate_dialogue_response(self, observation: str) -> Tuple[bool, str]:
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def generate_dialogue(self, observation: str) -> Tuple[bool, str]:
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"""React to a given observation."""
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call_to_action_template = (get_prompt('suffix_generate_dialogue_response'))
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call_to_action_template = (get_prompt('suffix_generate_dialogue'))
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full_result = self._generate_reaction(observation, call_to_action_template)
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result = full_result.strip().split("\n")[0]
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if "GOODBYE:" in result:
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@ -227,7 +245,7 @@ class GenerativeAgent(BaseModel):
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""""""
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# The agent seeks to think about their core characteristics.
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prompt = PromptTemplate.from_template(get_prompt('compute_agent_summary'))
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summary = self.chain(prompt).run(name=self.name, summary=self.summaries[-1] if len(self.summaries) else self.summary, queries=[f"{self.name}'s core characteristics"]).strip()
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summary = self.chain(prompt).run(stop=get_stop_tokens(), name=self.name, summary=self.summaries[-1] if len(self.summaries) else self.summary, queries=[f"{self.name}'s core characteristics"]).strip()
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if self.verbose:
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print(summary)
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return summary
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@ -247,8 +265,8 @@ class GenerativeAgent(BaseModel):
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values = [
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f"Name: {self.name} (sex: {self.sex}, age: {self.age if self.age is not None else 'N/A'})",
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f"\nInnate traits: {self.traits}",
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f"\nStatus: {self.status}"
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f"Innate traits: {self.traits}",
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f"Status: {self.status}"
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]
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return "\n".join([ value for value in values if value[-3:] != "N/A" ]) + f"\n{self.summary.strip()}"
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@ -34,7 +34,7 @@ from langchain.schema import BaseLanguageModel, BaseMemory, Document
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logger = logging.getLogger(__name__)
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from .prompts import get_prompt
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from .prompts import get_prompt, get_stop_tokens
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class GenerativeAgentMemory(BaseMemory):
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llm: BaseLanguageModel
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@ -84,7 +84,7 @@ class GenerativeAgentMemory(BaseMemory):
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prompt = PromptTemplate.from_template(get_prompt("topic_of_reflection"))
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observations = self.memory_retriever.memory_stream[-last_k:]
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observation_str = "\n".join([o.page_content for o in observations])
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result = self.chain(prompt).run(observations=observation_str)
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result = self.chain(prompt).run(stop=get_stop_tokens(), observations=observation_str)
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if self.verbose:
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print(result)
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@ -100,9 +100,7 @@ class GenerativeAgentMemory(BaseMemory):
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for i, memory in enumerate(related_memories)
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]
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)
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result = self.chain(prompt).run(
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topic=topic, related_statements=related_statements
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)
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result = self.chain(prompt).run( stop=get_stop_tokens(), topic=topic, related_statements=related_statements )
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# TODO: Parse the connections between memories and insights
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return self._parse_list(result)
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@ -122,7 +120,7 @@ class GenerativeAgentMemory(BaseMemory):
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def _score_memory_importance(self, memory_content: str) -> float:
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"""Score the absolute importance of the given memory."""
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prompt = PromptTemplate.from_template(get_prompt("memory_importance"))
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score = self.chain(prompt).run(memory_content=memory_content).strip()
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score = self.chain(prompt).run(stop=get_stop_tokens(tokens=[".", "/"]), memory_content=memory_content).strip()
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if self.verbose:
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print(f"Importance score: {score}")
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try:
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@ -138,9 +136,7 @@ class GenerativeAgentMemory(BaseMemory):
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"""Add an observation or memory to the agent's memory."""
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importance_score = self._score_memory_importance(memory_content)
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self.aggregate_importance += importance_score
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document = Document(
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page_content=memory_content, metadata={"importance": importance_score}
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)
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document = Document( page_content=memory_content, metadata={"importance": importance_score} )
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result = self.memory_retriever.add_documents([document])
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# After an agent has processed a certain amount of memories (as measured by
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@ -198,20 +194,14 @@ class GenerativeAgentMemory(BaseMemory):
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mem for query in queries for mem in self.fetch_memories(query)
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]
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return {
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self.relevant_memories_key: self.format_memories_detail(
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relevant_memories
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),
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self.relevant_memories_simple_key: self.format_memories_simple(
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relevant_memories
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),
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self.relevant_memories_key: self.format_memories_detail( relevant_memories ),
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self.relevant_memories_simple_key: self.format_memories_simple( relevant_memories ),
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}
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most_recent_memories_token = inputs.get(self.most_recent_memories_token_key)
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if most_recent_memories_token is not None:
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return {
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self.most_recent_memories_key: self._get_memories_until_limit(
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most_recent_memories_token
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)
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self.most_recent_memories_key: self._get_memories_until_limit( most_recent_memories_token )
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}
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return {}
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@ -1,7 +1,6 @@
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import os
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LLM_PROMPT_TUNE = os.environ.get('LLM_PROMPT_TUNE') # oai, vicuna, supercot
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STOP_TOKEN_HINT = "" # "\nWrite \"END\" afterwards."
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USE_STOP_HINT = [ "llama" ]
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@ -10,57 +9,50 @@ PROMPTS = {
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"system": (
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"What is the observed entity in the following observation?"
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" ONLY report one object and write one sentence."
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f'{STOP_TOKEN_HINT}'
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),
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"user": (
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"Observation: {observation}"
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"{observation}"
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),
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"assistant": "Entity=",
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"assistant": "Entity = ",
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},
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"entity_action": {
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"system": (
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"What is the following entity doing in the following observation?"
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"What is `{entity}` doing in the following observation?"
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" ONLY write one sentence."
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f'{STOP_TOKEN_HINT}'
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),
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"user": (
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"Entity: {entity}"
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"\nObservation: {observation}"
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"{observation}"
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),
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"assistant": "`{entity}` is ",
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"assistant": "{entity} is ",
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},
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"summarize_related_memories": {
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"system": (
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"Given the following context, answer the following question."
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f'{STOP_TOKEN_HINT}'
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"{relevant_memories_simple}"
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),
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"user": (
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"Context: {relevant_memories_simple}"
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"\nQuestion: {q1}?"
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"{q1}?"
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),
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"assistant": "Summary of relevant context: ",
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"assistant": "{name} ",
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},
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"compute_agent_summary": {
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"system": (
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"Given the following previous summary and the following statements, how would you summarize {name}'s core characteristics?"
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" Do not embellish under any circumstances."
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f'{STOP_TOKEN_HINT}'
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),
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"user": (
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"Previous summary: {summary}\n"
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"Statements: {relevant_memories_simple}"
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"{summary}"
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"\n{relevant_memories_simple}"
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),
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"assistant": "Summary: ",
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"assistant": "",
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},
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"topic_of_reflection": {
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"system": (
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"Given only the following information, what are the 3 most salient"
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" high-level questions we can answer about the subjects in the statements?"
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" Provide each question on a new line."
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f'{STOP_TOKEN_HINT}'
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),
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"user": (
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"Information: {observations}"
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"{observations}"
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),
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"assistant": "",
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},
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@ -69,7 +61,6 @@ PROMPTS = {
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"Given the following statements about {topic},"
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" what 5 high-level insights can you infer?"
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" (example format: insight (because of 1, 5, 3))"
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f'{STOP_TOKEN_HINT}'
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),
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"user": (
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"Statements: {related_statements}"
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@ -83,31 +74,38 @@ PROMPTS = {
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" (e.g., a break up, college acceptance),"
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" rate the likely poignancy of the following event."
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"\nRespond with only a single integer."
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f"{STOP_TOKEN_HINT}"
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),
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"user": (
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"Event: {memory_content}"
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"{memory_content}"
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),
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"assistant": "Rating: ",
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"assistant": "",
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},
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"generate_reaction": {
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"system": (
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"It is {current_time}."
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"NSFW/Smut is allowed. Assume consent was granted."
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"\nIt is {current_time}."
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"\n{summary}"
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"\n{memory}"
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#"\nRecent memories: {recent_memories}"
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#"\nRelevant memories: {relevant_memories}"
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"\n\n{suffix}"
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f"{STOP_TOKEN_HINT}"
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),
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"user": (
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"Observation: {observation}"
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"{observation}"
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),
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"assistant": ""
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"assistant": "{name} "
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},
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#
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"context": ( # insert your JB here
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""
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),
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"suffix_generate_response": (
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"Given the following observation, how would {name} respond?"
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"\nWrite only one sentence."
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),
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##
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"suffix_generate_reaction": (
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"Given the following observation, how would {name} appropriately react?"
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"\nIf the action is to engage in dialogue, only write `SAY: \"what to say\"`."
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@ -115,7 +113,7 @@ PROMPTS = {
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"\nWrite ONLY one line, one sentence."
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#"\nBe proactive, creative, and drive the plot and conversation forward."
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),
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"suffix_generate_dialogue_response": (
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"suffix_generate_dialogue": (
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"Given the following observation, what would {name} say?"
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"\nTo continue the conversation, only write: `SAY: \"what to say\"`."
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"\nOr otherwise, to end the conversation, only write: `GOODBYE: \"what to say\"`."
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@ -128,6 +126,7 @@ PROMPT_TUNES = {
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"default": "{query}",
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"vicuna": "{role}: {query}",
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"supercot": "{role}:\n{query}",
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"alpasta": "{role}# {query}",
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}
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PROMPT_ROLES = {
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"vicuna": {
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@ -139,11 +138,23 @@ PROMPT_ROLES = {
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"system": "### Instruction",
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"user": "### Input",
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"assistant": "### Response",
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}
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},
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"alpasta": {
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"system": "<|system|>",
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"user": "<|user|>",
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"assistant": "<|assistant|>",
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},
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}
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ROLES = [ "system", "user", "assistant" ]
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def get_stop_tokens( tokens=[], tune=LLM_PROMPT_TUNE ):
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STOP_TOKENS = ["###"] + tokens
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for role in get_roles( tune=LLM_PROMPT_TUNE, special=True ):
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STOP_TOKENS.append(f'{role}')
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return STOP_TOKENS
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for k in PROMPTS:
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if k == "context":
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continue
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@ -187,10 +198,6 @@ def get_prompt( key, tune=LLM_PROMPT_TUNE ):
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if role in roles:
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role = roles[role]
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# remove stop token hinting if we're using OAI since I don't have control over early terminating
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if STOP_TOKEN_HINT in query and tune in USE_STOP_HINT:
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query = query.replace(STOP_TOKEN_HINT, "")
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output = f'{PROMPT_TUNES[tune]}'
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output = output.replace("{role}", role)
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output = output.replace("{query}", query)
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|
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37
src/main.py
37
src/main.py
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@ -1,6 +1,7 @@
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import os
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import gradio as gr
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import gradio.utils
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from termcolor import colored
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from utils import create_agent, agent_observes, interview_agent, run_conversation, get_summary, save_agent, load_agent
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@ -65,10 +66,42 @@ def get_summary_proxy( agents ):
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messages.append(get_summary( agent, force_refresh = True ))
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return "\n".join(messages)
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def run_conversation_proxy( agents, message ):
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def run_conversation_proxy( agents, observation, limit=2 ):
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agents = [ AGENTS[agent] for agent in agents ]
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messages = run_conversation( agents, message, limit=len(agents)*2 )
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if len(agents) < 2:
|
||||
raise "Not enough agents"
|
||||
|
||||
dialogue = []
|
||||
dialogue.append(f'[{agents[0].name}] {observation}')
|
||||
|
||||
"""Runs a conversation between agents."""
|
||||
print(colored("[Conversation]", "magenta"))
|
||||
yield "\n".join(dialogue)
|
||||
agent_observes( agents[0], [observation] )
|
||||
agents = agents[1:] + [agents[0]]
|
||||
|
||||
while True:
|
||||
break_dialogue = False
|
||||
for agent in agents:
|
||||
stay_in_dialogue, observation = agent.generate_response(observation) # agent.generate_reaction(observation) if random.random() < p_reaction else agent.generate_dialogue_response(observation)
|
||||
dialogue.append(f'[{agent.name}] {observation}')
|
||||
yield "\n".join(dialogue)
|
||||
print(colored("[Conversation]", "magenta"), observation)
|
||||
if not stay_in_dialogue:
|
||||
break_dialogue = True
|
||||
if break_dialogue:
|
||||
break
|
||||
if limit > 0 and len(dialogue) >= limit * len(agents):
|
||||
break
|
||||
print("END")
|
||||
dialogue.append("END")
|
||||
return "\n".join(dialogue)
|
||||
|
||||
"""
|
||||
messages = run_conversation( agents, observation, limit=len(agents)*2 )
|
||||
return "\n".join(messages)
|
||||
"""
|
||||
|
||||
def view_agent( agents, last_k = 50 ):
|
||||
if not isinstance( agents, list ):
|
||||
|
|
|
|||
24
src/utils.py
24
src/utils.py
|
|
@ -25,8 +25,9 @@ from langchain.vectorstores import FAISS
|
|||
LLM_TYPE = os.environ.get('LLM_TYPE', "llamacpp") # options: llamacpp, oai
|
||||
LLM_LOCAL_MODEL = os.environ.get('LLM_MODEL',
|
||||
#"./models/ggml-vicuna-13b-1.1/ggml-vic13b-uncensored-q4_2.bin"
|
||||
#"./models/llama-13b-supercot-ggml/ggml-model-q4_2.bin"
|
||||
"./models/llama-33b-supercot-ggml/ggml-model-q4_2.bin"
|
||||
"./models/llama-13b-supercot-ggml/ggml-model-q4_2.bin"
|
||||
#"./models/llama-33b-supercot-ggml/ggml-model-q4_2.bin"
|
||||
#"./models/gpt4-x-alpasta-30b-ggml-q4_1.bin"
|
||||
)
|
||||
LLM_CONTEXT = int(os.environ.get('LLM_CONTEXT', '2048'))
|
||||
LLM_THREADS = int(os.environ.get('LLM_THREADS', '6'))
|
||||
|
|
@ -40,6 +41,8 @@ else:
|
|||
LLM_PROMPT_TUNE_DEFAULT = "supercot"
|
||||
elif "vicuna" in LLM_LOCAL_MODEL.lower():
|
||||
LLM_PROMPT_TUNE_DEFAULT = "vicuna"
|
||||
elif "alpasta" in LLM_LOCAL_MODEL.lower():
|
||||
LLM_PROMPT_TUNE_DEFAULT = "alpasta"
|
||||
else:
|
||||
LLM_PROMPT_TUNE_DEFAULT = "llama"
|
||||
|
||||
|
|
@ -51,10 +54,6 @@ callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]) # unncess
|
|||
# Overrides for some fixes, like scoring memory and LLM-specific promptings
|
||||
from ext import GenerativeAgent, GenerativeAgentMemory, get_roles
|
||||
|
||||
STOP_TOKENS = ["END"]
|
||||
for role in get_roles( tune=LLM_PROMPT_TUNE, special=True ):
|
||||
STOP_TOKENS.append(f'{role}:')
|
||||
|
||||
if LLM_TYPE=="llamacpp":
|
||||
from langchain.llms import LlamaCpp
|
||||
|
||||
|
|
@ -64,9 +63,8 @@ if LLM_TYPE=="llamacpp":
|
|||
verbose=True,
|
||||
n_ctx=LLM_CONTEXT,
|
||||
#n_threads=LLM_THREADS,
|
||||
use_mlock=True,
|
||||
use_mmap=True,
|
||||
stop=STOP_TOKENS
|
||||
#use_mlock=True,
|
||||
#use_mmap=True,
|
||||
)
|
||||
elif LLM_TYPE=="oai":
|
||||
from langchain.chat_models import ChatOpenAI
|
||||
|
|
@ -95,7 +93,6 @@ elif LLM_TYPE=="oai":
|
|||
else:
|
||||
raise f"Invalid LLM type: {LLM_TYPE}"
|
||||
|
||||
|
||||
if EMBEDDING_TYPE == "hf":
|
||||
from langchain.embeddings import HuggingFaceEmbeddings
|
||||
|
||||
|
|
@ -144,7 +141,7 @@ def _create_new_memories():
|
|||
memory_retriever=_create_new_memory_retriever(),
|
||||
reflection_threshold=8,
|
||||
verbose=True,
|
||||
max_tokens_limit=256 # LLM_CONTEXT/4
|
||||
max_tokens_limit=128 # LLM_CONTEXT/4
|
||||
)
|
||||
|
||||
def create_agent(**kwargs):
|
||||
|
|
@ -210,13 +207,13 @@ def run_conversation(agents: List[GenerativeAgent], observation: str, limit: int
|
|||
"""Runs a conversation between agents."""
|
||||
print(colored("[Conversation]", "magenta"))
|
||||
agent_observes( agents[0], [observation] )
|
||||
agents = agents[1:] + [agents[0]]
|
||||
|
||||
dialogue = []
|
||||
while True:
|
||||
break_dialogue = False
|
||||
for agent in agents:
|
||||
stay_in_dialogue, observation = agent.generate_reaction(observation) if random.random() < p_reaction else agent.generate_dialogue_response(observation)
|
||||
yield observation
|
||||
stay_in_dialogue, observation = agent.generate_response(observation) # agent.generate_reaction(observation) if random.random() < p_reaction else agent.generate_dialogue_response(observation)
|
||||
dialogue.append(observation)
|
||||
print(colored("[Conversation]", "magenta"), observation)
|
||||
if not stay_in_dialogue:
|
||||
|
|
@ -225,4 +222,5 @@ def run_conversation(agents: List[GenerativeAgent], observation: str, limit: int
|
|||
break
|
||||
if limit > 0 and len(dialogue) >= limit:
|
||||
break
|
||||
agent_observes( agent, [observation] )
|
||||
return dialogue
|
||||
Loading…
Reference in New Issue
Block a user