mirror of
https://github.com/hwchase17/langchain.git
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Bagatur/revert revert nuclia (#8833)
This commit is contained in:
Bagatur
GitHub
144
docs/extras/integrations/document_loaders/nuclia.ipynb
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144
docs/extras/integrations/document_loaders/nuclia.ipynb
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{
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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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"metadata": {},
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"source": [
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"# Nuclia Understanding API document loader\n",
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"\n",
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"[Nuclia](https://nuclia.com) automatically indexes your unstructured data from any internal and external source, providing optimized search results and generative answers. It can handle video and audio transcription, image content extraction, and document parsing.\n",
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"\n",
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"The Nuclia Understanding API supports the processing of unstructured data, including text, web pages, documents, and audio/video contents. It extracts all texts wherever they are (using speech-to-text or OCR when needed), it also extracts metadata, embedded files (like images in a PDF), and web links. If machine learning is enabled, it identifies entities, provides a summary of the content and generates embeddings for all the sentences.\n",
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"\n",
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"To use the Nuclia Understanding API, you need to have a Nuclia account. You can create one for free 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 --upgrade protobuf\n",
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"#!pip install nucliadb-protos"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"os.environ[\"NUCLIA_ZONE\"] = \"<YOUR_ZONE>\" # e.g. europe-1\n",
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"os.environ[\"NUCLIA_NUA_KEY\"] = \"<YOUR_API_KEY>\""
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To use the Nuclia document loader, you need to instantiate a `NucliaUnderstandingAPI` tool:"
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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.tools.nuclia import NucliaUnderstandingAPI\n",
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"\n",
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"nua = NucliaUnderstandingAPI(enable_ml=False)"
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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.document_loaders.nuclia import NucliaLoader\n",
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"\n",
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"loader = NucliaLoader(\"./interview.mp4\", nua)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can now call the `load` the document in a loop until you get the document."
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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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"import time\n",
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"\n",
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"pending = True\n",
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"while pending:\n",
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" time.sleep(15)\n",
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" docs = loader.load()\n",
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" if len(docs) > 0:\n",
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" print(docs[0].page_content)\n",
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" print(docs[0].metadata)\n",
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" pending = False\n",
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" else:\n",
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" print(\"waiting...\")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Retrieved information\n",
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"\n",
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"Nuclia returns the following information:\n",
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"\n",
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"- file metadata\n",
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"- extracted text\n",
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"- nested text (like text in an embedded image)\n",
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"- paragraphs and sentences splitting (defined by the position of their first and last characters, plus start time and end time for a video or audio file)\n",
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"- links\n",
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"- a thumbnail\n",
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"- embedded files\n",
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"\n",
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"Note:\n",
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"\n",
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" Generated files (thumbnail, extracted embedded files, etc.) are provided as a token. You can download them with the [`/processing/download` endpoint](https://docs.nuclia.dev/docs/api#operation/Download_binary_file_processing_download_get).\n",
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"\n",
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" Also at any level, if an attribute exceeds a certain size, it will be put in a downloadable file and will be replaced in the document by a file pointer. This will consist of `{\"file\": {\"uri\": \"JWT_TOKEN\"}}`. The rule is that if the size of the message is greater than 1000000 characters, the biggest parts will be moved to downloadable files. First, the compression process will target vectors. If that is not enough, it will target large field metadata, and finally it will target extracted text.\n"
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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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@@ -0,0 +1,103 @@
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{
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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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"metadata": {},
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"source": [
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"# Nuclia Understanding API document transformer\n",
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"\n",
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"[Nuclia](https://nuclia.com) automatically indexes your unstructured data from any internal and external source, providing optimized search results and generative answers. It can handle video and audio transcription, image content extraction, and document parsing.\n",
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"\n",
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"The Nuclia Understanding API document transformer splits text into paragraphs and sentences, identifies entities, provides a summary of the text and generates embeddings for all the sentences.\n",
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"\n",
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"To use the Nuclia Understanding API, you need to have a Nuclia account. You can create one for free at [https://nuclia.cloud](https://nuclia.cloud), and then [create a NUA key](https://docs.nuclia.dev/docs/docs/using/understanding/intro).\n",
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"\n",
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"from langchain.document_transformers.nuclia_text_transform import NucliaTextTransformer"
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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 --upgrade protobuf\n",
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"#!pip install nucliadb-protos"
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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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"import os\n",
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"\n",
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"os.environ[\"NUCLIA_ZONE\"] = \"<YOUR_ZONE>\" # e.g. europe-1\n",
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"os.environ[\"NUCLIA_NUA_KEY\"] = \"<YOUR_API_KEY>\""
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To use the Nuclia document transformer, you need to instantiate a `NucliaUnderstandingAPI` tool with `enable_ml` set to `True`:"
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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.tools.nuclia import NucliaUnderstandingAPI\n",
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"\n",
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"nua = NucliaUnderstandingAPI(enable_ml=True)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The Nuclia document transformer must be called in async mode, so you need to use the `atransform_documents` method:"
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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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"import asyncio\n",
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"\n",
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"from langchain.document_transformers.nuclia_text_transform import NucliaTextTransformer\n",
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"from langchain.schema.document import Document\n",
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"\n",
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"\n",
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"async def process():\n",
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" documents = [\n",
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" Document(page_content=\"<TEXT 1>\", metadata={}),\n",
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" Document(page_content=\"<TEXT 2>\", metadata={}),\n",
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" Document(page_content=\"<TEXT 3>\", metadata={}),\n",
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" ]\n",
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" nuclia_transformer = NucliaTextTransformer(nua)\n",
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" transformed_documents = await nuclia_transformer.atransform_documents(documents)\n",
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" print(transformed_documents)\n",
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"\n",
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"\n",
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"asyncio.run(process())"
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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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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173
docs/extras/integrations/tools/nuclia.ipynb
Normal file
173
docs/extras/integrations/tools/nuclia.ipynb
Normal file
@@ -0,0 +1,173 @@
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{
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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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"metadata": {},
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"source": [
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"# Nuclia Understanding API tool\n",
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"\n",
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"[Nuclia](https://nuclia.com) automatically indexes your unstructured data from any internal and external source, providing optimized search results and generative answers. It can handle video and audio transcription, image content extraction, and document parsing.\n",
|
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"\n",
|
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"The Nuclia Understanding API supports the processing of unstructured data, including text, web pages, documents, and audio/video contents. It extracts all texts wherever it is (using speech-to-text or OCR when needed), it identifies entities, it aslo extracts metadata, embedded files (like images in a PDF), and web links. It also provides a summary of the content.\n",
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"\n",
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"To use the Nuclia Understanding API, you need to have a Nuclia account. You can create one for free 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 --upgrade protobuf\n",
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"#!pip install nucliadb-protos"
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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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"import os\n",
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"\n",
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"os.environ[\"NUCLIA_ZONE\"] = \"<YOUR_ZONE>\" # e.g. europe-1\n",
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"os.environ[\"NUCLIA_NUA_KEY\"] = \"<YOUR_API_KEY>\""
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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.tools.nuclia import NucliaUnderstandingAPI\n",
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"\n",
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"nua = NucliaUnderstandingAPI(enable_ml=False)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can push files to the Nuclia Understanding API using the `push` action. As the processing is done asynchronously, the results might be returned in a different order than the files were pushed. That is why you need to provide an `id` to match the results with the corresponding file."
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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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"nua.run({\"action\": \"push\", \"id\": \"1\", \"path\": \"./report.docx\"})\n",
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"nua.run({\"action\": \"push\", \"id\": \"2\", \"path\": \"./interview.mp4\"})"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can now call the `pull` action in a loop until you get the JSON-formatted result."
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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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"import time\n",
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"\n",
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"pending = True\n",
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"data = None\n",
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"while pending:\n",
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" time.sleep(15)\n",
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" data = nua.run({\"action\": \"pull\", \"id\": \"1\", \"path\": None})\n",
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" if data:\n",
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" print(data)\n",
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" pending = False\n",
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" else:\n",
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" print(\"waiting...\")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can also do it in one step in `async` mode, you only need to do a push, and it will wait until the results are pulled:"
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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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"import asyncio\n",
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"\n",
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"\n",
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"async def process():\n",
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" data = await nua.arun(\n",
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" {\"action\": \"push\", \"id\": \"1\", \"path\": \"./talk.mp4\", \"text\": None}\n",
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" )\n",
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" print(data)\n",
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"\n",
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"\n",
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"asyncio.run(process())"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Retrieved information\n",
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"\n",
|
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"Nuclia returns the following information:\n",
|
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"\n",
|
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"- file metadata\n",
|
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"- extracted text\n",
|
||||
"- nested text (like text in an embedded image)\n",
|
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"- a summary (only when `enable_ml` is set to `True`)\n",
|
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"- paragraphs and sentences splitting (defined by the position of their first and last characters, plus start time and end time for a video or audio file)\n",
|
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"- named entities: people, dates, places, organizations, etc. (only when `enable_ml` is set to `True`)\n",
|
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"- links\n",
|
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"- a thumbnail\n",
|
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"- embedded files\n",
|
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"- the vector representations of the text (only when `enable_ml` is set to `True`)\n",
|
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"\n",
|
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"Note:\n",
|
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"\n",
|
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" Generated files (thumbnail, extracted embedded files, etc.) are provided as a token. You can download them with the [`/processing/download` endpoint](https://docs.nuclia.dev/docs/api#operation/Download_binary_file_processing_download_get).\n",
|
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"\n",
|
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" Also at any level, if an attribute exceeds a certain size, it will be put in a downloadable file and will be replaced in the document by a file pointer. This will consist of `{\"file\": {\"uri\": \"JWT_TOKEN\"}}`. The rule is that if the size of the message is greater than 1000000 characters, the biggest parts will be moved to downloadable files. First, the compression process will target vectors. If that is not enough, it will target large field metadata, and finally it will target extracted text.\n"
|
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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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