more tuning
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@ -86,20 +86,6 @@ class GenerativeAgent(BaseModel):
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llm=self.llm, prompt=prompt, verbose=self.verbose, memory=self.memory
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)
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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(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(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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def get_most_recent_memories(self, last_k: int = 4) -> str:
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memories = self.memory.memory_retriever.memory_stream[-last_k:]
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return [ document.page_content for document in memories ]
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@ -107,18 +93,8 @@ class GenerativeAgent(BaseModel):
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def summarize_related_memories(self, observation: str) -> str:
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"""Summarize memories that are most relevant to an observation."""
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prompt = PromptTemplate.from_template(get_prompt('summarize_related_memories'))
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q1 = f"What is the relationship between the subjects in that interaction?"
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q1 = f"Summarize the relationship between the subjects in that interaction."
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summary = self.chain(prompt=prompt).run(name=self.name, stop=get_stop_tokens(), q1=q1, observation=observation, queries=[observation]).strip()
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"""
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entity_name = self._get_entity_from_observation(observation).split("\n")[0].strip()
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q1 = f"What is the relationship between {self.name} and {entity_name}"
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if self.name.strip() in entity_name:
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return "N/A"
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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(name=self.name, stop=get_stop_tokens(), q1=q1, queries=[q1, q2]).strip()
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"""
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return f'{self.name} {summary}'
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#return self.chain(prompt=prompt).run(stop=get_stop_tokens(), q1=q1, q2=q2).strip()
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@ -151,7 +127,15 @@ class GenerativeAgent(BaseModel):
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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(stop=get_stop_tokens(), **kwargs).strip()
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reaction = self.chain(prompt=prompt).run(stop=get_stop_tokens(), queries=[observation], **kwargs).strip()
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# cleanup
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reactions = reaction.replace(u"\u200B", "").strip().split("\n")
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for reaction in reactions:
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if reaction:
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break
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if self.verbose:
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print(reaction)
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return reaction
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@ -173,73 +157,6 @@ class GenerativeAgent(BaseModel):
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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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candidates = full_result.replace(u"\u200B", "").strip().split("\n")
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response = ""
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results = []
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for candidate in candidates:
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if "REACT:" in candidate or "SAY:" in candidate:
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# can't be assed to iteratively replace
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candidate = candidate.strip().replace("React:", "REACT:").replace("Say:", "SAY:")
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results.append(f'{candidate}'.replace("SAY:", "said").replace(f"REACT: {self.name}", "").replace("REACT:", ""))
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if len(results) > 0:
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response = " and ".join(results).strip().replace(" ", " ")
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valid = True
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else:
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response = f"did not react in a relevant way"
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valid = False
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# AAA
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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: f"{self.name} observed: {observation}; {self.name}'s reaction: {response}"
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},
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)
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return valid, f"{self.name} {response}"
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"""
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if "REACT:" in result:
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reaction = self._clean_response(result.split("REACT:")[-1])
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return True, f"{self.name} {reaction}"
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if "SAY:" in result:
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said_value = self._clean_response(result.split("SAY:")[-1])
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return True, f"{self.name} said {said_value}"
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else:
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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(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'))
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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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farewell = self._clean_response(result.split("GOODBYE:")[-1])
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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: f"{self.name} observed: {observation}; {self.name}'s farewell response: {farewell}"
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},
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)
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return False, f"{self.name} said {farewell}"
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if "SAY:" in result:
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response_text = self._clean_response(result.split("SAY:")[-1])
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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: f"{self.name} observed: {observation}; {self.name}'s response: {response_text}"
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},
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)
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return True, f"{self.name} said {response_text}"
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else:
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return False, result
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######################################################
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# Agent stateful' summary methods. #
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# Each dialog or response prompt includes a header #
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@ -27,11 +27,11 @@ PROMPTS = {
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},
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"summarize_related_memories": {
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"system": (
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"{relevant_memories_simple}"
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"{observation}"
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"{q1}"
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),
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"user": (
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"{q1}?"
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"{relevant_memories_simple}"
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"{observation}"
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),
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"assistant": "{name} ",
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},
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@ -83,9 +83,9 @@ PROMPTS = {
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},
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"generate_reaction": {
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"system": (
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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{relevant_memories_simple}"
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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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15
src/main.py
15
src/main.py
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@ -95,19 +95,14 @@ def run_conversation_proxy( agents, observation, limit=2 ):
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agent_observes( agents[0], [observation] )
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agents = agents[1:] + [agents[0]]
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dialogue = []
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while True:
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break_dialogue = False
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for agent in agents:
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stay_in_dialogue, observation = agent.generate_response(observation) # agent.generate_reaction(observation) if random.random() < p_reaction else agent.generate_dialogue_response(observation)
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dialogue.append(f'[{agent.name}] {observation}')
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yield "\n".join(dialogue)
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print(colored("[Conversation]", "magenta"), observation)
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if not stay_in_dialogue:
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break_dialogue = True
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if break_dialogue:
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break
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if limit > 0 and len(dialogue) >= limit * len(agents):
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observation = agent_reacts( agent, [ observation ] )[0]
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yield observation
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if limit > 0 and len(dialogue) >= limit:
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break
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return dialogue
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print("END")
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dialogue.append("END")
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return "\n".join(dialogue)
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@ -223,7 +223,7 @@ def run_conversation(agents: List[GenerativeAgent], observation: str, limit: int
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dialogue = []
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while True:
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for agent in agents:
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_, observation = agent_reacts( agent, [ observation ] )
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observation = agent_reacts( agent, [ observation ] )[0]
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if limit > 0 and len(dialogue) >= limit:
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break
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return dialogue
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