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61 lines
2.2 KiB
Python
61 lines
2.2 KiB
Python
"""Chain that carries on a conversation and calls an LLM."""
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from typing import Dict, List
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from pydantic import BaseModel, Extra, Field, root_validator
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from langchain.chains.conversation.prompt import PROMPT
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from langchain.chains.llm import LLMChain
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from langchain.memory.buffer import ConversationBufferMemory
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from langchain.prompts.base import BasePromptTemplate
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from langchain.schema import BaseMemory
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class ConversationChain(LLMChain, BaseModel):
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"""Chain to have a conversation and load context from memory.
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Example:
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.. code-block:: python
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from langchain import ConversationChain, OpenAI
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conversation = ConversationChain(llm=OpenAI())
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"""
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memory: BaseMemory = Field(default_factory=ConversationBufferMemory)
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"""Default memory store."""
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prompt: BasePromptTemplate = PROMPT
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"""Default conversation prompt to use."""
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input_key: str = "input" #: :meta private:
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output_key: str = "response" #: :meta private:
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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arbitrary_types_allowed = True
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@property
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def input_keys(self) -> List[str]:
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"""Use this since so some prompt vars come from history."""
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return [self.input_key]
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@root_validator()
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def validate_prompt_input_variables(cls, values: Dict) -> Dict:
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"""Validate that prompt input variables are consistent."""
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memory_keys = values["memory"].memory_variables
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input_key = values["input_key"]
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if input_key in memory_keys:
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raise ValueError(
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f"The input key {input_key} was also found in the memory keys "
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f"({memory_keys}) - please provide keys that don't overlap."
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)
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prompt_variables = values["prompt"].input_variables
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expected_keys = memory_keys + [input_key]
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if set(expected_keys) != set(prompt_variables):
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raise ValueError(
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"Got unexpected prompt input variables. The prompt expects "
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f"{prompt_variables}, but got {memory_keys} as inputs from "
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f"memory, and {input_key} as the normal input key."
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)
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return values
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