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75 lines
2.3 KiB
Python
75 lines
2.3 KiB
Python
from __future__ import annotations
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from typing import TYPE_CHECKING, List, Optional
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from langchain.schema import BaseRetriever, Document
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if TYPE_CHECKING:
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from zep_python import SearchResult
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class ZepRetriever(BaseRetriever):
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"""A Retriever implementation for the Zep long-term memory store. Search your
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user's long-term chat history with Zep.
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Note: You will need to provide the user's `session_id` to use this retriever.
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More on Zep:
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Zep provides long-term conversation storage for LLM apps. The server stores,
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summarizes, embeds, indexes, and enriches conversational AI chat
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histories, and exposes them via simple, low-latency APIs.
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For server installation instructions, see:
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https://getzep.github.io/deployment/quickstart/
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"""
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def __init__(
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self,
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session_id: str,
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url: str,
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top_k: Optional[int] = None,
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):
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try:
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from zep_python import ZepClient
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except ImportError:
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raise ValueError(
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"Could not import zep-python package. "
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"Please install it with `pip install zep-python`."
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)
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self.zep_client = ZepClient(base_url=url)
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self.session_id = session_id
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self.top_k = top_k
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def _search_result_to_doc(self, results: List[SearchResult]) -> List[Document]:
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return [
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Document(
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page_content=r.message.pop("content"),
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metadata={"score": r.dist, **r.message},
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)
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for r in results
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if r.message
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]
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def get_relevant_documents(self, query: str) -> List[Document]:
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from zep_python import SearchPayload
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payload: SearchPayload = SearchPayload(text=query)
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results: List[SearchResult] = self.zep_client.search_memory(
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self.session_id, payload, limit=self.top_k
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)
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return self._search_result_to_doc(results)
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async def aget_relevant_documents(self, query: str) -> List[Document]:
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from zep_python import SearchPayload
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payload: SearchPayload = SearchPayload(text=query)
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results: List[SearchResult] = await self.zep_client.asearch_memory(
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self.session_id, payload, limit=self.top_k
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)
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return self._search_result_to_doc(results)
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