mirror of
https://github.com/hwchase17/langchain
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a26105de8e
Description: another Vectara template for MultiQuery RAG flow Twitter handle: @ofermend Fixes to #13106 --------- Co-authored-by: Ofer Mendelevitch <ofer@vectara.com> Co-authored-by: Ofer Mendelevitch <ofermend@gmail.com>
58 lines
1.2 KiB
Plaintext
58 lines
1.2 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "8692a430",
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"metadata": {},
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"source": [
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"# Run Template\n",
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"\n",
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"In `server.py`, set -\n",
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"```\n",
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"add_routes(app, chain_ext, path=\"/rag-vectara-multiquery\")\n",
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"```"
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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": "41db5e30",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langserve.client import RemoteRunnable\n",
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"\n",
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"rag_app_vectara = RemoteRunnable(\"http://localhost:8000/rag-vectara-multiquery\")\n",
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"rag_app_vectara.invoke(\"How does agent memory work?\")"
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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 3.11.6 64-bit",
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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": "3.11.6"
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},
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"vscode": {
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"interpreter": {
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"hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
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}
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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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