mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
c010ec8b71
- `.get_relevant_documents(query)` -> `.invoke(query)`
- `.get_relevant_documents(query=query)` -> `.invoke(query)`
- `.get_relevant_documents(query, callbacks=callbacks)` ->
`.invoke(query, config={"callbacks": callbacks})`
- `.get_relevant_documents(query, **kwargs)` -> `.invoke(query,
**kwargs)`
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
34 lines
1.1 KiB
Python
34 lines
1.1 KiB
Python
from typing import Any, Dict, List
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from langchain.memory.chat_memory import BaseChatMemory
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from langchain.memory.utils import get_prompt_input_key
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from langchain_core.vectorstores import VectorStoreRetriever
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from langchain_experimental.pydantic_v1 import Field
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class AutoGPTMemory(BaseChatMemory):
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"""Memory for AutoGPT."""
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retriever: VectorStoreRetriever = Field(exclude=True)
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"""VectorStoreRetriever object to connect to."""
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@property
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def memory_variables(self) -> List[str]:
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return ["chat_history", "relevant_context"]
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def _get_prompt_input_key(self, inputs: Dict[str, Any]) -> str:
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"""Get the input key for the prompt."""
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if self.input_key is None:
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return get_prompt_input_key(inputs, self.memory_variables)
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return self.input_key
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def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
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input_key = self._get_prompt_input_key(inputs)
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query = inputs[input_key]
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docs = self.retriever.invoke(query)
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return {
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"chat_history": self.chat_memory.messages[-10:],
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"relevant_context": docs,
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}
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