mirror of
https://github.com/hwchase17/langchain
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59 lines
1.5 KiB
Plaintext
59 lines
1.5 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": "681a5d1e",
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"metadata": {},
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"source": [
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"## Connect to template"
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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": 3,
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"id": "d774be2a",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'The agent memory consists of two components: short-term memory and long-term memory. The short-term memory is used for in-context learning and allows the model to learn from its experiences. The long-term memory enables the agent to retain and recall an infinite amount of information over extended periods by leveraging an external vector store and fast retrieval.'"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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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_pinecone = RemoteRunnable(\"http://localhost:8001/rag_pinecone_rerank\")\n",
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"rag_app_pinecone.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 (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": "3.9.16"
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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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