forked from Archives/langchain
Zep Retriever - Vector Search Over Chat History (#4533)
# Zep Retriever - Vector Search Over Chat History with the Zep Long-term Memory Service More on Zep: https://github.com/getzep/zep Note: This PR is related to and relies on https://github.com/hwchase17/langchain/pull/4834. I did not want to modify the `pyproject.toml` file to add the `zep-python` dependency a second time. Co-authored-by: Daniel Chalef <daniel.chalef@private.org>searx_updates
Daniel Chalef
1 year ago
committed by
GitHub
parent
5525b704cc
commit
c8c2276ccb
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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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@ -0,0 +1,111 @@
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from __future__ import annotations
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import copy
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from typing import TYPE_CHECKING, List
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import pytest
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from pytest_mock import MockerFixture
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from langchain.retrievers import ZepRetriever
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from langchain.schema import Document
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if TYPE_CHECKING:
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from zep_python import SearchResult, ZepClient
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@pytest.fixture
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def search_results() -> List[SearchResult]:
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from zep_python import Message, SearchResult
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search_result = [
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{
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"message": {
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"uuid": "66830914-19f5-490b-8677-1ba06bcd556b",
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"created_at": "2023-05-18T20:40:42.743773Z",
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"role": "user",
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"content": "I'm looking to plan a trip to Iceland. Can you help me?",
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"token_count": 17,
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},
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"summary": None,
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"dist": 0.8734284910450115,
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},
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{
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"message": {
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"uuid": "015e618c-ba9d-45b6-95c3-77a8e611570b",
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"created_at": "2023-05-18T20:40:42.743773Z",
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"role": "user",
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"content": "How much does a trip to Iceland typically cost?",
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"token_count": 12,
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},
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"summary": None,
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"dist": 0.8554048017463456,
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},
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]
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return [
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SearchResult(
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message=Message.parse_obj(result["message"]),
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summary=result["summary"],
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dist=result["dist"],
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)
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for result in search_result
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]
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@pytest.fixture
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@pytest.mark.requires("zep_python")
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def zep_retriever(
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mocker: MockerFixture, search_results: List[SearchResult]
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) -> ZepRetriever:
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mock_zep_client: ZepClient = mocker.patch("zep_python.ZepClient", autospec=True)
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mock_zep_client.search_memory.return_value = copy.deepcopy( # type: ignore
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search_results
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)
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mock_zep_client.asearch_memory.return_value = copy.deepcopy( # type: ignore
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search_results
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)
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zep = ZepRetriever(session_id="123", url="http://localhost:8000")
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zep.zep_client = mock_zep_client
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return zep
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@pytest.mark.requires("zep_python")
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def test_zep_retriever_get_relevant_documents(
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zep_retriever: ZepRetriever, search_results: List[SearchResult]
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) -> None:
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documents: List[Document] = zep_retriever.get_relevant_documents(
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query="My trip to Iceland"
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)
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_test_documents(documents, search_results)
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@pytest.mark.requires("zep_python")
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@pytest.mark.asyncio
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async def test_zep_retriever_aget_relevant_documents(
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zep_retriever: ZepRetriever, search_results: List[SearchResult]
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) -> None:
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documents: List[Document] = await zep_retriever.aget_relevant_documents(
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query="My trip to Iceland"
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)
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_test_documents(documents, search_results)
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def _test_documents(
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documents: List[Document], search_results: List[SearchResult]
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) -> None:
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assert len(documents) == 2
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for i, document in enumerate(documents):
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assert document.page_content == search_results[i].message.get( # type: ignore
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"content"
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)
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assert document.metadata.get("uuid") == search_results[
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i
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].message.get( # type: ignore
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"uuid"
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)
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assert document.metadata.get("role") == search_results[
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i
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].message.get( # type: ignore
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"role"
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)
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assert document.metadata.get("score") == search_results[i].dist
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