mirror of
https://github.com/hwchase17/langchain
synced 2024-11-04 06:00:26 +00:00
fa5d49f2c1
ran ```bash g grep -l "langchain.vectorstores" | xargs -L 1 sed -i '' "s/langchain\.vectorstores/langchain_community.vectorstores/g" g grep -l "langchain.document_loaders" | xargs -L 1 sed -i '' "s/langchain\.document_loaders/langchain_community.document_loaders/g" g grep -l "langchain.chat_loaders" | xargs -L 1 sed -i '' "s/langchain\.chat_loaders/langchain_community.chat_loaders/g" g grep -l "langchain.document_transformers" | xargs -L 1 sed -i '' "s/langchain\.document_transformers/langchain_community.document_transformers/g" g grep -l "langchain\.graphs" | xargs -L 1 sed -i '' "s/langchain\.graphs/langchain_community.graphs/g" g grep -l "langchain\.memory\.chat_message_histories" | xargs -L 1 sed -i '' "s/langchain\.memory\.chat_message_histories/langchain_community.chat_message_histories/g" gco master libs/langchain/tests/unit_tests/*/test_imports.py gco master libs/langchain/tests/unit_tests/**/test_public_api.py ```
77 lines
1.6 KiB
Plaintext
77 lines
1.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "4ae4b789",
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"metadata": {},
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"source": [
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"## Document Loading\n",
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"\n",
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"Load a blog post on agents."
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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": "5d6bd62e",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.document_loaders import WebBaseLoader\n",
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"\n",
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"loader = WebBaseLoader(\"https://lilianweng.github.io/posts/2023-06-23-agent/\")\n",
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"text = loader.load()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8e21575d",
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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=\"/extraction-anthropic-functions\")\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": "5fd794ec-a002-490e-8eb9-06ce3e6c2f14",
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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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"anthropic_function_model = RemoteRunnable(\n",
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" \"http://localhost:8001/extraction-anthropic-functions\"\n",
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")\n",
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"anthropic_function_model.invoke(text[0].page_content[0:1500])"
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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": "langserve",
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"language": "python",
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"name": "langserve"
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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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