mirror of
https://github.com/hwchase17/langchain
synced 2024-11-04 06:00:26 +00:00
Implement NucliaDB vector store (#10236)
# Description This pull request allows to use the [NucliaDB](https://docs.nuclia.dev/docs/docs/nucliadb/intro) as a vector store in LangChain. It works with both a [local NucliaDB instance](https://docs.nuclia.dev/docs/docs/nucliadb/deploy/basics) or with [Nuclia Cloud](https://nuclia.cloud). # Dependencies It requires an up-to-date version of the `nuclia` Python package. @rlancemartin, @eyurtsev, @hinthornw, please review it when you have a moment :) Note: our Twitter handler is `@NucliaAI`
This commit is contained in:
Eric BREHAULT
GitHub
parent
b64a443f72
commit
19b4ecdc39
@ -18,7 +18,7 @@
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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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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -93,8 +93,22 @@
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "langchain",
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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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"name": "python"
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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": "3.10.5"
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},
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"orig_nbformat": 4
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},
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151
docs/extras/integrations/vectorstores/nucliadb_vectorstore.ipynb
Normal file
151
docs/extras/integrations/vectorstores/nucliadb_vectorstore.ipynb
Normal file
@ -0,0 +1,151 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# NucliaDB vector store\n",
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"\n",
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"You can use a local NucliaDB instance or use [Nuclia Cloud](https://nuclia.cloud).\n",
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"\n",
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"When using a local instance, you need a Nuclia Understanding API key, so your texts are properly vectorized and indexed. You can get a key by creating a free account at [https://nuclia.cloud](https://nuclia.cloud), and then [create a NUA key](https://docs.nuclia.dev/docs/docs/using/understanding/intro)."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"#!pip install nuclia"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Usage with nuclia.cloud"
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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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"metadata": {},
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"outputs": [
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{
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"ename": "ValueError",
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"evalue": "nuclia python package not found. Please install it with `pip install nuclia`.",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
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"File \u001b[0;32m~/dev/osprojects/langchain/libs/langchain/langchain/vectorstores/nucliadb.py:39\u001b[0m, in \u001b[0;36mNucliaDB.__init__\u001b[0;34m(self, knowledge_box, local, api_key, backend)\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 39\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mnuclia\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39msdk\u001b[39;00m \u001b[39mimport\u001b[39;00m NucliaAuth\n\u001b[1;32m 40\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mImportError\u001b[39;00m:\n",
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"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'nuclia'",
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"\nDuring handling of the above exception, another exception occurred:\n",
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"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[1], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mlangchain\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mvectorstores\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mnucliadb\u001b[39;00m \u001b[39mimport\u001b[39;00m NucliaDB\n\u001b[1;32m 2\u001b[0m API_KEY \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mYOUR_API_KEY\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m----> 4\u001b[0m ndb \u001b[39m=\u001b[39m NucliaDB(knowledge_box\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mYOUR_KB_ID\u001b[39;49m\u001b[39m\"\u001b[39;49m, local\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m, api_key\u001b[39m=\u001b[39;49mAPI_KEY)\n",
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"File \u001b[0;32m~/dev/osprojects/langchain/libs/langchain/langchain/vectorstores/nucliadb.py:41\u001b[0m, in \u001b[0;36mNucliaDB.__init__\u001b[0;34m(self, knowledge_box, local, api_key, backend)\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mnuclia\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39msdk\u001b[39;00m \u001b[39mimport\u001b[39;00m NucliaAuth\n\u001b[1;32m 40\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mImportError\u001b[39;00m:\n\u001b[0;32m---> 41\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 42\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mnuclia python package not found. \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 43\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mPlease install it with `pip install nuclia`.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 44\u001b[0m )\n\u001b[1;32m 45\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_config[\u001b[39m\"\u001b[39m\u001b[39mLOCAL\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m local\n\u001b[1;32m 46\u001b[0m zone \u001b[39m=\u001b[39m os\u001b[39m.\u001b[39menviron\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mNUCLIA_ZONE\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39meurope-1\u001b[39m\u001b[39m\"\u001b[39m)\n",
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"\u001b[0;31mValueError\u001b[0m: nuclia python package not found. Please install it with `pip install nuclia`."
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]
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}
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],
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"source": [
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"from langchain.vectorstores.nucliadb import NucliaDB\n",
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"API_KEY = \"YOUR_API_KEY\"\n",
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"\n",
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"ndb = NucliaDB(knowledge_box=\"YOUR_KB_ID\", local=False, api_key=API_KEY)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Usage with a local instance\n",
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"\n",
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"Note: By default `backend` is set to `http://localhost:8080`."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.vectorstores.nucliadb import NucliaDB\n",
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"\n",
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"ndb = NucliaDB(knowledge_box=\"YOUR_KB_ID\", local=True, backend=\"http://my-local-server\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Add and delete texts to your Knowledge Box"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"ids = ndb.add_texts([\"This is a new test\", \"This is a second test\"])"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"ndb.delete(ids=ids)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Search in your Knowledge Box"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"results = ndb.similarity_search(\"Who was inspired by Ada Lovelace?\")\n",
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"print(res.page_content)"
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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": "langchain",
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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": "3.10.5"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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159
libs/langchain/langchain/vectorstores/nucliadb.py
Normal file
159
libs/langchain/langchain/vectorstores/nucliadb.py
Normal file
@ -0,0 +1,159 @@
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import os
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from typing import Any, Dict, Iterable, List, Optional, Type
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from langchain.embeddings.base import Embeddings
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from langchain.schema.document import Document
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from langchain.vectorstores.base import VST, VectorStore
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FIELD_TYPES = {
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"f": "files",
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"t": "texts",
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"l": "links",
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}
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class NucliaDB(VectorStore):
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"""NucliaDB vector store."""
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_config: Dict[str, Any] = {}
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def __init__(
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self,
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knowledge_box: str,
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local: bool,
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api_key: Optional[str] = None,
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backend: Optional[str] = None,
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) -> None:
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"""Initialize the NucliaDB client.
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Args:
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knowledge_box: the Knowledge Box id.
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local: Whether to use a local NucliaDB instance or Nuclia Cloud
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api_key: A contributor API key for the kb (needed when local is False)
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backend: The backend url to use when local is True, defaults to
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http://localhost:8080
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"""
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try:
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from nuclia.sdk import NucliaAuth
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except ImportError:
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raise ValueError(
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"nuclia python package not found. "
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"Please install it with `pip install nuclia`."
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)
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self._config["LOCAL"] = local
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zone = os.environ.get("NUCLIA_ZONE", "europe-1")
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self._kb = knowledge_box
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if local:
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if not backend:
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backend = "http://localhost:8080"
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self._config["BACKEND"] = f"{backend}/api/v1"
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self._config["TOKEN"] = None
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NucliaAuth().nucliadb(url=backend)
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NucliaAuth().kb(url=self.kb_url, interactive=False)
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else:
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self._config["BACKEND"] = f"https://{zone}.nuclia.cloud/api/v1"
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self._config["TOKEN"] = api_key
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NucliaAuth().kb(
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url=self.kb_url, token=self._config["TOKEN"], interactive=False
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)
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@property
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def is_local(self) -> str:
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return self._config["LOCAL"]
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@property
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def kb_url(self) -> str:
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return f"{self._config['BACKEND']}/kb/{self._kb}"
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def add_texts(
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self,
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texts: Iterable[str],
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metadatas: Optional[List[dict]] = None,
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**kwargs: Any,
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) -> List[str]:
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"""Upload texts to NucliaDB"""
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ids = []
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from nuclia.sdk import NucliaResource
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factory = NucliaResource()
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for i, text in enumerate(texts):
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extra: Dict[str, Any] = {"metadata": ""}
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if metadatas:
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extra = {"metadata": metadatas[i]}
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id = factory.create(
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texts={"text": {"body": text}},
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extra=extra,
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url=self.kb_url,
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api_key=self._config["TOKEN"],
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)
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ids.append(id)
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return ids
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def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
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if not ids:
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return None
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from nuclia.sdk import NucliaResource
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factory = NucliaResource()
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results: List[bool] = []
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for id in ids:
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try:
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factory.delete(rid=id, url=self.kb_url, api_key=self._config["TOKEN"])
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results.append(True)
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except ValueError:
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results.append(False)
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return all(results)
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def similarity_search(
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self, query: str, k: int = 4, **kwargs: Any
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) -> List[Document]:
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from nuclia.sdk import NucliaSearch
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from nucliadb_models.search import FindRequest, ResourceProperties
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request = FindRequest(
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query=query,
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page_size=k,
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show=[ResourceProperties.VALUES, ResourceProperties.EXTRA],
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)
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search = NucliaSearch()
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results = search.find(
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query=request, url=self.kb_url, api_key=self._config["TOKEN"]
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)
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paragraphs = []
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for resource in results.resources.values():
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for field in resource.fields.values():
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for paragraph_id, paragraph in field.paragraphs.items():
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info = paragraph_id.split("/")
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field_type = FIELD_TYPES.get(info[1], None)
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field_id = info[2]
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if not field_type:
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continue
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value = getattr(resource.data, field_type, {}).get(field_id, None)
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paragraphs.append(
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{
|
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"text": paragraph.text,
|
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"metadata": {
|
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"extra": getattr(
|
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getattr(resource, "extra", {}), "metadata", None
|
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),
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"value": value,
|
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},
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"order": paragraph.order,
|
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}
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)
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sorted_paragraphs = sorted(paragraphs, key=lambda x: x["order"])
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return [
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Document(page_content=paragraph["text"], metadata=paragraph["metadata"])
|
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for paragraph in sorted_paragraphs
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]
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@classmethod
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def from_texts(
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cls: Type[VST],
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texts: List[str],
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embedding: Embeddings,
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metadatas: Optional[List[dict]] = None,
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**kwargs: Any,
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) -> VST:
|
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"""Return VectorStore initialized from texts and embeddings."""
|
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raise NotImplementedError
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452
libs/langchain/poetry.lock
generated
452
libs/langchain/poetry.lock
generated
@ -2440,6 +2440,20 @@ calc = ["shapely"]
|
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s3 = ["boto3 (>=1.3.1)"]
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test = ["Fiona[s3]", "pytest (>=7)", "pytest-cov", "pytz"]
|
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|
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[[package]]
|
||||
name = "fire"
|
||||
version = "0.5.0"
|
||||
description = "A library for automatically generating command line interfaces."
|
||||
optional = true
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "fire-0.5.0.tar.gz", hash = "sha256:a6b0d49e98c8963910021f92bba66f65ab440da2982b78eb1bbf95a0a34aacc6"},
|
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]
|
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|
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[package.dependencies]
|
||||
six = "*"
|
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termcolor = "*"
|
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|
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[[package]]
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name = "flatbuffers"
|
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version = "23.5.26"
|
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@ -2686,6 +2700,42 @@ files = [
|
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[package.dependencies]
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gitdb = ">=4.0.1,<5"
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|
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[[package]]
|
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name = "gnureadline"
|
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version = "8.1.2"
|
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description = "The standard Python readline extension statically linked against the GNU readline library."
|
||||
optional = true
|
||||
python-versions = "*"
|
||||
files = [
|
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{file = "gnureadline-8.1.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:861936c9b362d96152af2d73ccb6f3e901e70f0e4a2e7e62f4e226e91d349edb"},
|
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{file = "gnureadline-8.1.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:2ce5c49ecc54e1df0193e90422806a5940f908553206689aeaa04bc959d3aa9a"},
|
||||
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||||
[package.dependencies]
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||||
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||||
grpcio = ">=1.57.0"
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||||
protobuf = ">=4.21.6,<5.0dev"
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||||
setuptools = "*"
|
||||
|
||||
[[package]]
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||||
@ -3723,6 +3772,7 @@ optional = false
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[[package]]
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@ -5195,19 +5245,19 @@ files = [
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[[package]]
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||||
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||||
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||||
types-protobuf = ">=3.20.4"
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||||
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||||
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@ -5499,6 +5549,74 @@ jupyter-server = ">=1.8,<3"
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||||
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{file = "Pillow-9.5.0-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:84a6f19ce086c1bf894644b43cd129702f781ba5751ca8572f08aa40ef0ab7b7"},
|
||||
{file = "Pillow-9.5.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:1e7723bd90ef94eda669a3c2c19d549874dd5badaeefabefd26053304abe5799"},
|
||||
{file = "Pillow-9.5.0.tar.gz", hash = "sha256:bf548479d336726d7a0eceb6e767e179fbde37833ae42794602631a070d630f1"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
@ -6429,34 +6557,24 @@ wcwidth = "*"
|
||||
|
||||
[[package]]
|
||||
name = "protobuf"
|
||||
version = "3.20.3"
|
||||
description = "Protocol Buffers"
|
||||
category = "main"
|
||||
version = "4.22.3"
|
||||
description = ""
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "protobuf-3.20.3-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:f4bd856d702e5b0d96a00ec6b307b0f51c1982c2bf9c0052cf9019e9a544ba99"},
|
||||
{file = "protobuf-3.20.3-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:9aae4406ea63d825636cc11ffb34ad3379335803216ee3a856787bcf5ccc751e"},
|
||||
{file = "protobuf-3.20.3-cp310-cp310-win32.whl", hash = "sha256:28545383d61f55b57cf4df63eebd9827754fd2dc25f80c5253f9184235db242c"},
|
||||
{file = "protobuf-3.20.3-cp310-cp310-win_amd64.whl", hash = "sha256:67a3598f0a2dcbc58d02dd1928544e7d88f764b47d4a286202913f0b2801c2e7"},
|
||||
{file = "protobuf-3.20.3-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:899dc660cd599d7352d6f10d83c95df430a38b410c1b66b407a6b29265d66469"},
|
||||
{file = "protobuf-3.20.3-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:e64857f395505ebf3d2569935506ae0dfc4a15cb80dc25261176c784662cdcc4"},
|
||||
{file = "protobuf-3.20.3-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:d9e4432ff660d67d775c66ac42a67cf2453c27cb4d738fc22cb53b5d84c135d4"},
|
||||
{file = "protobuf-3.20.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:74480f79a023f90dc6e18febbf7b8bac7508420f2006fabd512013c0c238f454"},
|
||||
{file = "protobuf-3.20.3-cp37-cp37m-win32.whl", hash = "sha256:b6cc7ba72a8850621bfec987cb72623e703b7fe2b9127a161ce61e61558ad905"},
|
||||
{file = "protobuf-3.20.3-cp37-cp37m-win_amd64.whl", hash = "sha256:8c0c984a1b8fef4086329ff8dd19ac77576b384079247c770f29cc8ce3afa06c"},
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||||
{file = "protobuf-3.20.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:de78575669dddf6099a8a0f46a27e82a1783c557ccc38ee620ed8cc96d3be7d7"},
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||||
{file = "protobuf-3.20.3-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:f4c42102bc82a51108e449cbb32b19b180022941c727bac0cfd50170341f16ee"},
|
||||
{file = "protobuf-3.20.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:44246bab5dd4b7fbd3c0c80b6f16686808fab0e4aca819ade6e8d294a29c7050"},
|
||||
{file = "protobuf-3.20.3-cp38-cp38-win32.whl", hash = "sha256:c02ce36ec760252242a33967d51c289fd0e1c0e6e5cc9397e2279177716add86"},
|
||||
{file = "protobuf-3.20.3-cp38-cp38-win_amd64.whl", hash = "sha256:447d43819997825d4e71bf5769d869b968ce96848b6479397e29fc24c4a5dfe9"},
|
||||
{file = "protobuf-3.20.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:398a9e0c3eaceb34ec1aee71894ca3299605fa8e761544934378bbc6c97de23b"},
|
||||
{file = "protobuf-3.20.3-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:bf01b5720be110540be4286e791db73f84a2b721072a3711efff6c324cdf074b"},
|
||||
{file = "protobuf-3.20.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:daa564862dd0d39c00f8086f88700fdbe8bc717e993a21e90711acfed02f2402"},
|
||||
{file = "protobuf-3.20.3-cp39-cp39-win32.whl", hash = "sha256:819559cafa1a373b7096a482b504ae8a857c89593cf3a25af743ac9ecbd23480"},
|
||||
{file = "protobuf-3.20.3-cp39-cp39-win_amd64.whl", hash = "sha256:03038ac1cfbc41aa21f6afcbcd357281d7521b4157926f30ebecc8d4ea59dcb7"},
|
||||
{file = "protobuf-3.20.3-py2.py3-none-any.whl", hash = "sha256:a7ca6d488aa8ff7f329d4c545b2dbad8ac31464f1d8b1c87ad1346717731e4db"},
|
||||
{file = "protobuf-3.20.3.tar.gz", hash = "sha256:2e3427429c9cffebf259491be0af70189607f365c2f41c7c3764af6f337105f2"},
|
||||
{file = "protobuf-4.22.3-cp310-abi3-win32.whl", hash = "sha256:8b54f56d13ae4a3ec140076c9d937221f887c8f64954673d46f63751209e839a"},
|
||||
{file = "protobuf-4.22.3-cp310-abi3-win_amd64.whl", hash = "sha256:7760730063329d42a9d4c4573b804289b738d4931e363ffbe684716b796bde51"},
|
||||
{file = "protobuf-4.22.3-cp37-abi3-macosx_10_9_universal2.whl", hash = "sha256:d14fc1a41d1a1909998e8aff7e80d2a7ae14772c4a70e4bf7db8a36690b54425"},
|
||||
{file = "protobuf-4.22.3-cp37-abi3-manylinux2014_aarch64.whl", hash = "sha256:70659847ee57a5262a65954538088a1d72dfc3e9882695cab9f0c54ffe71663b"},
|
||||
{file = "protobuf-4.22.3-cp37-abi3-manylinux2014_x86_64.whl", hash = "sha256:13233ee2b9d3bd9a5f216c1fa2c321cd564b93d8f2e4f521a85b585447747997"},
|
||||
{file = "protobuf-4.22.3-cp37-cp37m-win32.whl", hash = "sha256:ecae944c6c2ce50dda6bf76ef5496196aeb1b85acb95df5843cd812615ec4b61"},
|
||||
{file = "protobuf-4.22.3-cp37-cp37m-win_amd64.whl", hash = "sha256:d4b66266965598ff4c291416be429cef7989d8fae88b55b62095a2331511b3fa"},
|
||||
{file = "protobuf-4.22.3-cp38-cp38-win32.whl", hash = "sha256:f08aa300b67f1c012100d8eb62d47129e53d1150f4469fd78a29fa3cb68c66f2"},
|
||||
{file = "protobuf-4.22.3-cp38-cp38-win_amd64.whl", hash = "sha256:f2f4710543abec186aee332d6852ef5ae7ce2e9e807a3da570f36de5a732d88e"},
|
||||
{file = "protobuf-4.22.3-cp39-cp39-win32.whl", hash = "sha256:7cf56e31907c532e460bb62010a513408e6cdf5b03fb2611e4b67ed398ad046d"},
|
||||
{file = "protobuf-4.22.3-cp39-cp39-win_amd64.whl", hash = "sha256:e0e630d8e6a79f48c557cd1835865b593d0547dce221c66ed1b827de59c66c97"},
|
||||
{file = "protobuf-4.22.3-py3-none-any.whl", hash = "sha256:52f0a78141078077cfe15fe333ac3e3a077420b9a3f5d1bf9b5fe9d286b4d881"},
|
||||
{file = "protobuf-4.22.3.tar.gz", hash = "sha256:23452f2fdea754a8251d0fc88c0317735ae47217e0d27bf330a30eec2848811a"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -9152,41 +9270,41 @@ tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
|
||||
|
||||
[[package]]
|
||||
name = "streamlit"
|
||||
version = "1.22.0"
|
||||
version = "1.26.0"
|
||||
description = "A faster way to build and share data apps"
|
||||
category = "main"
|
||||
optional = true
|
||||
python-versions = ">=3.7, !=3.9.7"
|
||||
python-versions = ">=3.8, !=3.9.7"
|
||||
files = [
|
||||
{file = "streamlit-1.22.0-py2.py3-none-any.whl", hash = "sha256:520dd9b9e6efb559b5a9a22feadb48b1e6f0340ec83da3514810059fdecd4167"},
|
||||
{file = "streamlit-1.22.0.tar.gz", hash = "sha256:5bef9bf8deef32814d9565c9df48331e6357eb0b90dabc3ec4f53c44fb34fc73"},
|
||||
{file = "streamlit-1.26.0-py2.py3-none-any.whl", hash = "sha256:2bfdac041816e2e1ba27f061d40112afe61e0d4e72d25f354b38ba81107b4cb3"},
|
||||
{file = "streamlit-1.26.0.tar.gz", hash = "sha256:25475fb15a3cc9fb184945f3fc936f011998bd8386e0c892febe14c9625bf47a"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
altair = ">=3.2.0,<5"
|
||||
blinker = ">=1.0.0"
|
||||
cachetools = ">=4.0"
|
||||
click = ">=7.0"
|
||||
gitpython = "!=3.1.19"
|
||||
importlib-metadata = ">=1.4"
|
||||
numpy = "*"
|
||||
packaging = ">=14.1"
|
||||
pandas = ">=0.25,<3"
|
||||
pillow = ">=6.2.0"
|
||||
protobuf = ">=3.12,<4"
|
||||
pyarrow = ">=4.0"
|
||||
pydeck = ">=0.1.dev5"
|
||||
pympler = ">=0.9"
|
||||
python-dateutil = "*"
|
||||
requests = ">=2.4"
|
||||
rich = ">=10.11.0"
|
||||
tenacity = ">=8.0.0,<9"
|
||||
toml = "*"
|
||||
tornado = ">=6.0.3"
|
||||
typing-extensions = ">=3.10.0.0"
|
||||
tzlocal = ">=1.1"
|
||||
validators = ">=0.2"
|
||||
watchdog = {version = "*", markers = "platform_system != \"Darwin\""}
|
||||
altair = ">=4.0,<6"
|
||||
blinker = ">=1.0.0,<2"
|
||||
cachetools = ">=4.0,<6"
|
||||
click = ">=7.0,<9"
|
||||
gitpython = ">=3.0.7,<3.1.19 || >3.1.19,<4"
|
||||
importlib-metadata = ">=1.4,<7"
|
||||
numpy = ">=1.19.3,<2"
|
||||
packaging = ">=16.8,<24"
|
||||
pandas = ">=1.3.0,<3"
|
||||
pillow = ">=7.1.0,<10"
|
||||
protobuf = ">=3.20,<5"
|
||||
pyarrow = ">=6.0"
|
||||
pydeck = ">=0.8,<1"
|
||||
pympler = ">=0.9,<2"
|
||||
python-dateutil = ">=2.7.3,<3"
|
||||
requests = ">=2.18,<3"
|
||||
rich = ">=10.14.0,<14"
|
||||
tenacity = ">=8.1.0,<9"
|
||||
toml = ">=0.10.1,<2"
|
||||
tornado = ">=6.0.3,<7"
|
||||
typing-extensions = ">=4.1.0,<5"
|
||||
tzlocal = ">=1.1,<5"
|
||||
validators = ">=0.2,<1"
|
||||
watchdog = {version = ">=2.1.5", markers = "platform_system != \"Darwin\""}
|
||||
|
||||
[package.extras]
|
||||
snowflake = ["snowflake-snowpark-python"]
|
||||
@ -10927,4 +11045,4 @@ text-helpers = ["chardet"]
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
content-hash = "b63078268a80c07577b432114302f4f86d47be25b83a245affb0dbc999fb2c1f"
|
||||
content-hash = "600bb53efb135b6058aed19ff13c41de9b27f18a85f14fb4fb425a89644001ec"
|
||||
|
||||
@ -129,6 +129,7 @@ markdownify = {version = "^0.11.6", optional = true}
|
||||
assemblyai = {version = "^0.17.0", optional = true}
|
||||
dashvector = {version = "^1.0.1", optional = true}
|
||||
sqlite-vss = {version = "^0.1.2", optional = true}
|
||||
nuclia = {version = "^1.1.10", optional = true}
|
||||
|
||||
|
||||
[tool.poetry.group.test.dependencies]
|
||||
@ -345,6 +346,7 @@ extended_testing = [
|
||||
"markdownify",
|
||||
"dashvector",
|
||||
"sqlite-vss",
|
||||
"nuclia",
|
||||
]
|
||||
|
||||
[tool.ruff]
|
||||
|
||||
@ -0,0 +1,98 @@
|
||||
from typing import Any
|
||||
from unittest import mock
|
||||
|
||||
from langchain.vectorstores.nucliadb import NucliaDB
|
||||
|
||||
|
||||
class attrdict(dict):
|
||||
def __getitem__(self, key: str) -> Any:
|
||||
value = dict.__getitem__(self, key)
|
||||
return attrdict(value) if isinstance(value, dict) else value
|
||||
|
||||
__getattr__ = __getitem__
|
||||
|
||||
|
||||
def FakeCreate(**args: Any) -> Any:
|
||||
def fn(self: Any, **kwargs: Any) -> str:
|
||||
return "fake_uuid"
|
||||
|
||||
return fn
|
||||
|
||||
|
||||
def FakeDelete(**args: Any) -> Any:
|
||||
def fn(self: Any, **kwargs: Any) -> None:
|
||||
return None
|
||||
|
||||
return fn
|
||||
|
||||
|
||||
def FakeFind(**args: Any) -> Any:
|
||||
def fn(self: Any, **kwargs: Any) -> Any:
|
||||
return attrdict(
|
||||
{
|
||||
"resources": {
|
||||
"123": attrdict(
|
||||
{
|
||||
"fields": {
|
||||
"456": attrdict(
|
||||
{
|
||||
"paragraphs": {
|
||||
"123/t/text/0-14": attrdict(
|
||||
{
|
||||
"text": "This is a test",
|
||||
"order": 0,
|
||||
}
|
||||
),
|
||||
}
|
||||
}
|
||||
)
|
||||
},
|
||||
"data": {
|
||||
"texts": {
|
||||
"text": {
|
||||
"body": "This is a test",
|
||||
}
|
||||
}
|
||||
},
|
||||
"extra": attrdict({"metadata": {"some": "metadata"}}),
|
||||
}
|
||||
)
|
||||
}
|
||||
}
|
||||
)
|
||||
|
||||
return fn
|
||||
|
||||
|
||||
def test_add_texts() -> None:
|
||||
with mock.patch(
|
||||
"nuclia.sdk.resource.NucliaResource.create",
|
||||
new_callable=FakeCreate,
|
||||
):
|
||||
ndb = NucliaDB(knowledge_box="YOUR_KB_ID", local=False, api_key="YOUR_API_KEY")
|
||||
assert ndb.is_local is False
|
||||
ids = ndb.add_texts(["This is a new test", "This is a second test"])
|
||||
assert len(ids) == 2
|
||||
|
||||
|
||||
def test_delete() -> None:
|
||||
with mock.patch(
|
||||
"nuclia.sdk.resource.NucliaResource.delete",
|
||||
new_callable=FakeDelete,
|
||||
):
|
||||
ndb = NucliaDB(knowledge_box="YOUR_KB_ID", local=False, api_key="YOUR_API_KEY")
|
||||
success = ndb.delete(["123", "456"])
|
||||
assert success
|
||||
|
||||
|
||||
def test_search() -> None:
|
||||
with mock.patch(
|
||||
"nuclia.sdk.search.NucliaSearch.find",
|
||||
new_callable=FakeFind,
|
||||
):
|
||||
ndb = NucliaDB(knowledge_box="YOUR_KB_ID", local=False, api_key="YOUR_API_KEY")
|
||||
results = ndb.similarity_search("Who was inspired by Ada Lovelace?")
|
||||
assert len(results) == 1
|
||||
assert results[0].page_content == "This is a test"
|
||||
assert results[0].metadata["extra"]["some"] == "metadata"
|
||||
assert results[0].metadata["value"]["body"] == "This is a test"
|
||||
Loading…
Reference in New Issue
Block a user