forked from Archives/langchain
Harrison/vespa (#3761)
Co-authored-by: Lester Solbakken <lesters@users.noreply.github.com>fix_agent_callbacks
Harrison Chase
1 year ago
committed by
GitHub
parent
ee20b3e0d0
commit
c55ba43093
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "ce0f17b9",
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"metadata": {},
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"source": [
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"# Vespa retriever\n",
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"\n",
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"This notebook shows how to use Vespa.ai as a LangChain retriever.\n",
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"Vespa.ai is a platform for highly efficient structured text and vector search.\n",
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"Please refer to [Vespa.ai](https://vespa.ai) for more information.\n",
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"\n",
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"In order to create a retriever, we use [pyvespa](https://pyvespa.readthedocs.io/en/latest/index.html) to\n",
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"create a connection a Vespa service."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "c10dd962",
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"metadata": {},
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"outputs": [],
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"source": [
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"from vespa.application import Vespa\n",
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"\n",
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"vespa_app = Vespa(url=\"https://doc-search.vespa.oath.cloud\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3df4ce53",
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"metadata": {},
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"source": [
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"This creates a connection to a Vespa service, here the Vespa documentation search service.\n",
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"Using pyvespa, you can also connect to a\n",
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"[Vespa Cloud instance](https://pyvespa.readthedocs.io/en/latest/deploy-vespa-cloud.html)\n",
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"or a local\n",
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"[Docker instance](https://pyvespa.readthedocs.io/en/latest/deploy-docker.html).\n",
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"\n",
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"\n",
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"After connecting to the service, you can set up the retriever:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7ccca1f4",
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"from langchain.retrievers.vespa_retriever import VespaRetriever\n",
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"\n",
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"vespa_query_body = {\n",
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" \"yql\": \"select content from paragraph where userQuery()\",\n",
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" \"hits\": 5,\n",
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" \"ranking\": \"documentation\",\n",
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" \"locale\": \"en-us\"\n",
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"}\n",
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"vespa_content_field = \"content\"\n",
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"retriever = VespaRetriever(vespa_app, vespa_query_body, vespa_content_field)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1e7e34e1",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"This sets up a LangChain retriever that fetches documents from the Vespa application.\n",
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"Here, up to 5 results are retrieved from the `content` field in the `paragraph` document type,\n",
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"using `doumentation` as the ranking method. The `userQuery()` is replaced with the actual query\n",
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"passed from LangChain.\n",
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"\n",
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"Please refer to the [pyvespa documentation](https://pyvespa.readthedocs.io/en/latest/getting-started-pyvespa.html#Query)\n",
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"for more information.\n",
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"\n",
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"Now you can return the results and continue using the results in LangChain."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "f47a2bfe",
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"retriever.get_relevant_documents(\"what is vespa?\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -0,0 +1,44 @@
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"""Wrapper for retrieving documents from Vespa."""
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from __future__ import annotations
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import json
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from typing import TYPE_CHECKING, List
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from langchain.schema import BaseRetriever, Document
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if TYPE_CHECKING:
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from vespa.application import Vespa
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class VespaRetriever(BaseRetriever):
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def __init__(self, app: Vespa, body: dict, content_field: str):
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self._application = app
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self._query_body = body
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self._content_field = content_field
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def get_relevant_documents(self, query: str) -> List[Document]:
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body = self._query_body.copy()
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body["query"] = query
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response = self._application.query(body)
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if not str(response.status_code).startswith("2"):
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raise RuntimeError(
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"Could not retrieve data from Vespa. Error code: {}".format(
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response.status_code
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)
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)
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root = response.json["root"]
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if "errors" in root:
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raise RuntimeError(json.dumps(root["errors"]))
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hits = []
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for child in response.hits:
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page_content = child["fields"][self._content_field]
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metadata = {"id": child["id"]}
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hits.append(Document(page_content=page_content, metadata=metadata))
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return hits
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async def aget_relevant_documents(self, query: str) -> List[Document]:
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raise NotImplementedError
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