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agent multi inputs
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e2e501aa06
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@ -37,7 +37,7 @@ class Agent(Chain, BaseModel, ABC):
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:meta private:
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"""
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return [self.input_key]
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return set(self.llm_chain.input_keys) - {"thoughts"}
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@property
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def output_keys(self) -> List[str]:
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@ -99,23 +99,24 @@ class Agent(Chain, BaseModel, ABC):
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llm_chain = LLMChain(llm=llm, prompt=cls.create_prompt(tools))
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return cls(llm_chain=llm_chain, tools=tools, **kwargs)
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def get_action(self, text: str) -> Action:
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def get_action(self, thoughts: str, inputs: dict) -> Action:
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"""Given input, decided what to do.
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Args:
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text: input string
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thoughts: LLM thoughts
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inputs: user inputs
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Returns:
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Action specifying what tool to use.
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"""
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input_key = self.llm_chain.input_keys[0]
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inputs = {input_key: text, "stop": self._stop}
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full_output = self.llm_chain.predict(**inputs)
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new_inputs = {"thoughts": thoughts, "stop": self._stop}
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full_inputs = {**inputs, **new_inputs}
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full_output = self.llm_chain.predict(**full_inputs)
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parsed_output = self._extract_tool_and_input(full_output)
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while parsed_output is None:
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full_output = self._fix_text(full_output)
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inputs = {input_key: text + full_output, "stop": self._stop}
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output = self.llm_chain.predict(**inputs)
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full_inputs["thoughts"] += full_output
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output = self.llm_chain.predict(**full_inputs)
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full_output += output
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parsed_output = self._extract_tool_and_input(full_output)
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tool, tool_input = parsed_output
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@ -123,19 +124,12 @@ class Agent(Chain, BaseModel, ABC):
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def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
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"""Run text through and get agent response."""
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text = inputs[self.input_key]
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# Do any preparation necessary when receiving a new input.
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self._prepare_for_new_call()
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# Construct a mapping of tool name to tool for easy lookup
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name_to_tool_map = {tool.name: tool.func for tool in self.tools}
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# Construct the initial string to pass into the LLM. This is made up
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# of the user input, the special starter string, and then the LLM prefix.
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# The starter string is a special string that may be used by a LLM to
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# immediately follow the user input. The LLM prefix is a string that
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# prompts the LLM to take an action.
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starter_string = text + self.starter_string + self.llm_prefix
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# We use the ChainedInput class to iteratively add to the input over time.
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chained_input = ChainedInput(starter_string, verbose=self.verbose)
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chained_input = ChainedInput(self.llm_prefix, verbose=self.verbose)
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# We construct a mapping from each tool to a color, used for logging.
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color_mapping = get_color_mapping(
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[tool.name for tool in self.tools], excluded_colors=["green"]
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@ -143,7 +137,7 @@ class Agent(Chain, BaseModel, ABC):
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# We now enter the agent loop (until it returns something).
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while True:
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# Call the LLM to see what to do.
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output = self.get_action(chained_input.input)
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output = self.get_action(chained_input.input, inputs)
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# Add the log to the Chained Input.
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chained_input.add(output.log, color="green")
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# If the tool chosen is the finishing tool, then we end and return.
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@ -85,7 +85,7 @@ class ZeroShotAgent(Agent):
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format_instructions = FORMAT_INSTRUCTIONS.format(tool_names=tool_names)
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template = "\n\n".join([prefix, tool_strings, format_instructions, suffix])
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if input_variables is None:
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input_variables = ["input"]
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input_variables = ["input", "thoughts"]
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return PromptTemplate(template=template, input_variables=input_variables)
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@classmethod
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@ -12,4 +12,4 @@ Thought: I now know the final answer
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Final Answer: the final answer to the original input question"""
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SUFFIX = """Begin!
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Question: {input}"""
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Question: {input}{thoughts}"""
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