Spaces:
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Sleeping
Merge pull request #14 from eliawaefler/langchain
Browse files- .gitignore +7 -0
- langchain_vectara.ipynb +900 -0
- langchain_vectara.py +134 -0
- requirements.txt +9 -1
- schema.py +130 -0
- together_call.py +44 -0
.gitignore
ADDED
@@ -0,0 +1,7 @@
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.envrc
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.direnv/
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flake.lock
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flake.nix
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.env
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.venv
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langchain_vectara.ipynb
ADDED
@@ -0,0 +1,900 @@
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"cells": [
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{
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"cell_type": "code",
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"source": [
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"!pip install -r requirements.txt"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "lVZX4hy1Ruq_",
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"outputId": "0c963932-2266-4c44-d671-07dc23625bae"
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},
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"execution_count": 2,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Requirement already satisfied: langchain_community in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 1)) (0.0.32)\n",
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"Requirement already satisfied: langchain-text-splitters in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 2)) (0.0.1)\n",
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"Requirement already satisfied: langchain-together in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 3)) (0.1.0)\n",
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"Requirement already satisfied: pdf2image in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 5)) (1.17.0)\n",
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"Requirement already satisfied: pdfminer.six in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 6)) (20231228)\n",
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"Requirement already satisfied: pillow_heif in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 7)) (0.16.0)\n",
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"Requirement already satisfied: doctran in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 8)) (0.0.14)\n",
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"Collecting python-dotenv (from -r requirements.txt (line 9))\n",
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" Downloading python_dotenv-1.0.1-py3-none-any.whl.metadata (23 kB)\n",
|
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"Requirement already satisfied: unstructured[local-inference] in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 4)) (0.13.2)\n",
|
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"Requirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.10/dist-packages (from langchain_community->-r requirements.txt (line 1)) (6.0.1)\n",
|
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+
"Requirement already satisfied: SQLAlchemy<3,>=1.4 in /usr/local/lib/python3.10/dist-packages (from langchain_community->-r requirements.txt (line 1)) (2.0.29)\n",
|
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+
"Requirement already satisfied: aiohttp<4.0.0,>=3.8.3 in /usr/local/lib/python3.10/dist-packages (from langchain_community->-r requirements.txt (line 1)) (3.9.3)\n",
|
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"Requirement already satisfied: dataclasses-json<0.7,>=0.5.7 in /usr/local/lib/python3.10/dist-packages (from langchain_community->-r requirements.txt (line 1)) (0.6.4)\n",
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"Requirement already satisfied: langchain-core<0.2.0,>=0.1.41 in /usr/local/lib/python3.10/dist-packages (from langchain_community->-r requirements.txt (line 1)) (0.1.42)\n",
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"Requirement already satisfied: langsmith<0.2.0,>=0.1.0 in /usr/local/lib/python3.10/dist-packages (from langchain_community->-r requirements.txt (line 1)) (0.1.47)\n",
|
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"Requirement already satisfied: numpy<2,>=1 in /usr/local/lib/python3.10/dist-packages (from langchain_community->-r requirements.txt (line 1)) (1.25.2)\n",
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+
"Requirement already satisfied: requests<3,>=2 in /usr/local/lib/python3.10/dist-packages (from langchain_community->-r requirements.txt (line 1)) (2.31.0)\n",
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+
"Requirement already satisfied: tenacity<9.0.0,>=8.1.0 in /usr/local/lib/python3.10/dist-packages (from langchain_community->-r requirements.txt (line 1)) (8.2.3)\n",
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"Requirement already satisfied: together<0.3.0,>=0.2.10 in /usr/local/lib/python3.10/dist-packages (from langchain-together->-r requirements.txt (line 3)) (0.2.11)\n",
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"Requirement already satisfied: chardet in /usr/local/lib/python3.10/dist-packages (from unstructured[local-inference]->-r requirements.txt (line 4)) (5.2.0)\n",
|
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"Requirement already satisfied: filetype in /usr/local/lib/python3.10/dist-packages (from unstructured[local-inference]->-r requirements.txt (line 4)) (1.2.0)\n",
|
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"Requirement already satisfied: python-magic in /usr/local/lib/python3.10/dist-packages (from unstructured[local-inference]->-r requirements.txt (line 4)) (0.4.27)\n",
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"Requirement already satisfied: lxml in /usr/local/lib/python3.10/dist-packages (from unstructured[local-inference]->-r requirements.txt (line 4)) (4.9.4)\n",
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"Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (from unstructured[local-inference]->-r requirements.txt (line 4)) (3.8.1)\n",
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"Requirement already satisfied: tabulate in /usr/local/lib/python3.10/dist-packages (from unstructured[local-inference]->-r requirements.txt (line 4)) (0.9.0)\n",
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"Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from unstructured[local-inference]->-r requirements.txt (line 4)) (4.12.3)\n",
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"Requirement already satisfied: emoji in /usr/local/lib/python3.10/dist-packages (from unstructured[local-inference]->-r requirements.txt (line 4)) (2.11.0)\n",
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"Requirement already satisfied: python-iso639 in /usr/local/lib/python3.10/dist-packages (from unstructured[local-inference]->-r requirements.txt (line 4)) (2024.2.7)\n",
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" Downloading langsmith-0.1.14-py3-none-any.whl.metadata (13 kB)\n",
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" Downloading langsmith-0.1.0-py3-none-any.whl.metadata (13 kB)\n",
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"Collecting langchain-core<0.2.0,>=0.1.41 (from langchain_community->-r requirements.txt (line 1))\n",
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" Downloading langchain_core-0.1.42-py3-none-any.whl.metadata (5.9 kB)\n",
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" Downloading langchain_core-0.1.41-py3-none-any.whl.metadata (5.9 kB)\n",
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"Collecting doctran (from -r requirements.txt (line 8))\n",
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" Downloading doctran-0.0.14-py3-none-any.whl.metadata (8.6 kB)\n",
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" Downloading doctran-0.0.10-py3-none-any.whl.metadata (8.9 kB)\n",
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"Collecting tiktoken<0.5.0,>=0.4.0 (from doctran->-r requirements.txt (line 8))\n",
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" Downloading tiktoken-0.4.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.2 kB)\n",
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"Collecting doctran (from -r requirements.txt (line 8))\n",
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" Downloading doctran-0.0.9-py3-none-any.whl.metadata (8.9 kB)\n",
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"Collecting tiktoken<0.4.0,>=0.3.3 (from doctran->-r requirements.txt (line 8))\n",
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" Downloading tiktoken-0.3.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.2 kB)\n",
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"Collecting doctran (from -r requirements.txt (line 8))\n",
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" Downloading doctran-0.0.8-py3-none-any.whl.metadata (8.9 kB)\n",
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"Collecting bs4<0.0.2,>=0.0.1 (from doctran->-r requirements.txt (line 8))\n",
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" Downloading bs4-0.0.1.tar.gz (1.1 kB)\n",
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" Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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"Requirement already satisfied: jsonschema<5.0.0,>=4.17.3 in /usr/local/lib/python3.10/dist-packages (from doctran->-r requirements.txt (line 8)) (4.19.2)\n",
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"Collecting mailbox<0.5,>=0.4 (from doctran->-r requirements.txt (line 8))\n",
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" Downloading mailbox-0.4.tar.gz (4.1 kB)\n",
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" Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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"Collecting pdfplumber<0.10.0,>=0.9.0 (from doctran->-r requirements.txt (line 8))\n",
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" Downloading pdfplumber-0.9.0-py3-none-any.whl.metadata (35 kB)\n",
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"Collecting doctran (from -r requirements.txt (line 8))\n",
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" Downloading doctran-0.0.1-py3-none-any.whl.metadata (5.5 kB)\n",
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" Downloading doctran-0.0.0-py3-none-any.whl.metadata (599 bytes)\n",
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"Collecting langchain-together (from -r requirements.txt (line 3))\n",
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" Downloading langchain_together-0.1.0-py3-none-any.whl.metadata (1.9 kB)\n",
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" Downloading langchain_together-0.0.1-py3-none-any.whl.metadata (504 bytes)\n",
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"Collecting langchain-text-splitters (from -r requirements.txt (line 2))\n",
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" Downloading langchain_text_splitters-0.0.1-py3-none-any.whl.metadata (2.0 kB)\n",
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"Collecting langchain_community (from -r requirements.txt (line 1))\n",
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" Downloading langchain_community-0.0.32-py3-none-any.whl.metadata (8.5 kB)\n",
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" Downloading langchain_community-0.0.31-py3-none-any.whl.metadata (8.4 kB)\n",
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"Collecting langchain-core<0.2.0,>=0.1.37 (from langchain_community->-r requirements.txt (line 1))\n",
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" Downloading langchain_core-0.1.40-py3-none-any.whl.metadata (5.9 kB)\n",
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" Downloading langchain_core-0.1.38-py3-none-any.whl.metadata (6.0 kB)\n",
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" Downloading langchain_core-0.1.37-py3-none-any.whl.metadata (6.0 kB)\n",
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"Collecting langchain_community (from -r requirements.txt (line 1))\n",
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" Downloading langchain_community-0.0.30-py3-none-any.whl.metadata (8.4 kB)\n",
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" Downloading langchain_community-0.0.29-py3-none-any.whl.metadata (8.3 kB)\n",
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"Collecting langchain-core<0.2.0,>=0.1.33 (from langchain_community->-r requirements.txt (line 1))\n",
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" Downloading langchain_core-0.1.36-py3-none-any.whl.metadata (6.0 kB)\n",
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" Downloading langchain_core-0.1.35-py3-none-any.whl.metadata (6.0 kB)\n",
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" Downloading langchain_core-0.1.34-py3-none-any.whl.metadata (6.0 kB)\n",
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" Downloading langchain_core-0.1.33-py3-none-any.whl.metadata (6.0 kB)\n",
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"Requirement already satisfied: anyio<5,>=3 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.2.0,>=0.1.33->langchain_community->-r requirements.txt (line 1)) (3.7.1)\n",
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"Collecting langchain_community (from -r requirements.txt (line 1))\n",
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" Downloading langchain_community-0.0.28-py3-none-any.whl.metadata (8.3 kB)\n",
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"Collecting langchain-core<0.2.0,>=0.1.31 (from langchain_community->-r requirements.txt (line 1))\n",
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" Downloading langchain_core-0.1.32-py3-none-any.whl.metadata (6.0 kB)\n",
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" Downloading langchain_core-0.1.31-py3-none-any.whl.metadata (6.0 kB)\n",
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"Collecting langchain_community (from -r requirements.txt (line 1))\n",
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" Downloading langchain_community-0.0.27-py3-none-any.whl.metadata (8.2 kB)\n",
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"Collecting langchain-core<0.2.0,>=0.1.30 (from langchain_community->-r requirements.txt (line 1))\n",
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" Downloading langchain_core-0.1.30-py3-none-any.whl.metadata (6.0 kB)\n",
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"Collecting langchain_community (from -r requirements.txt (line 1))\n",
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" Downloading langchain_community-0.0.26-py3-none-any.whl.metadata (8.2 kB)\n",
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"Collecting langchain-core<0.2.0,>=0.1.29 (from langchain_community->-r requirements.txt (line 1))\n",
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" Downloading langchain_core-0.1.29-py3-none-any.whl.metadata (6.0 kB)\n",
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"Collecting langchain_community (from -r requirements.txt (line 1))\n",
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" Downloading langchain_community-0.0.25-py3-none-any.whl.metadata (8.1 kB)\n",
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"\u001b[31mERROR: Exception:\n",
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"Traceback (most recent call last):\n",
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" File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/base_command.py\", line 180, in exc_logging_wrapper\n",
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+
" status = run_func(*args)\n",
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+
" File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/req_command.py\", line 245, in wrapper\n",
|
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+
" return func(self, options, args)\n",
|
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+
" File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/commands/install.py\", line 377, in run\n",
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287 |
+
" requirement_set = resolver.resolve(\n",
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+
" File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/resolution/resolvelib/resolver.py\", line 95, in resolve\n",
|
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+
" result = self._result = resolver.resolve(\n",
|
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+
" File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/resolvelib/resolvers.py\", line 546, in resolve\n",
|
291 |
+
" state = resolution.resolve(requirements, max_rounds=max_rounds)\n",
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+
" File \"/usr/local/lib/python3.10/dist-packages/pip/_vendor/resolvelib/resolvers.py\", line 457, in resolve\n",
|
293 |
+
" raise ResolutionTooDeep(max_rounds)\n",
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+
"pip._vendor.resolvelib.resolvers.ResolutionTooDeep: 200000\u001b[0m\u001b[31m\n",
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"\u001b[0m"
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]
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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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"source": [
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"!pip install langchain_community langchain-text-splitters unstructured[local-inference] pdf2image pdfminer.six langchain-together pillow_heif"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 1000
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},
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"id": "j06J9xE60u0C",
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"outputId": "06248856-b7d5-402f-f38d-03e475f2786b"
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},
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"execution_count": 1,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Collecting langchain_community\n",
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" Downloading langchain_community-0.0.32-py3-none-any.whl (1.9 MB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hCollecting langchain-text-splitters\n",
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" Downloading langchain_text_splitters-0.0.1-py3-none-any.whl (21 kB)\n",
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+
"Collecting unstructured[local-inference]\n",
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" Downloading unstructured-0.13.2-py3-none-any.whl (1.9 MB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hCollecting pdf2image\n",
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" Downloading pdf2image-1.17.0-py3-none-any.whl (11 kB)\n",
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"Collecting pdfminer.six\n",
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" Downloading pdfminer.six-20231228-py3-none-any.whl (5.6 MB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.6/5.6 MB\u001b[0m \u001b[31m21.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hCollecting langchain-together\n",
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+
" Downloading langchain_together-0.1.0-py3-none-any.whl (6.7 kB)\n",
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334 |
+
"Collecting pillow_heif\n",
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+
" Downloading pillow_heif-0.16.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.5 MB)\n",
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+
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.5/7.5 MB\u001b[0m \u001b[31m15.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hRequirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.10/dist-packages (from langchain_community) (6.0.1)\n",
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"Requirement already satisfied: SQLAlchemy<3,>=1.4 in /usr/local/lib/python3.10/dist-packages (from langchain_community) (2.0.29)\n",
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"Requirement already satisfied: aiohttp<4.0.0,>=3.8.3 in /usr/local/lib/python3.10/dist-packages (from langchain_community) (3.9.3)\n",
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"Collecting dataclasses-json<0.7,>=0.5.7 (from langchain_community)\n",
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" Downloading dataclasses_json-0.6.4-py3-none-any.whl (28 kB)\n",
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"Collecting langchain-core<0.2.0,>=0.1.41 (from langchain_community)\n",
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" Downloading langchain_core-0.1.42-py3-none-any.whl (287 kB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m287.5/287.5 kB\u001b[0m \u001b[31m12.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hCollecting langsmith<0.2.0,>=0.1.0 (from langchain_community)\n",
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" Downloading langsmith-0.1.47-py3-none-any.whl (113 kB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m113.0/113.0 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hRequirement already satisfied: numpy<2,>=1 in /usr/local/lib/python3.10/dist-packages (from langchain_community) (1.25.2)\n",
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"Requirement already satisfied: requests<3,>=2 in /usr/local/lib/python3.10/dist-packages (from langchain_community) (2.31.0)\n",
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"Requirement already satisfied: tenacity<9.0.0,>=8.1.0 in /usr/local/lib/python3.10/dist-packages (from langchain_community) (8.2.3)\n",
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+
"Requirement already satisfied: chardet in /usr/local/lib/python3.10/dist-packages (from unstructured[local-inference]) (5.2.0)\n",
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"Collecting filetype (from unstructured[local-inference])\n",
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" Downloading filetype-1.2.0-py2.py3-none-any.whl (19 kB)\n",
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"Collecting python-magic (from unstructured[local-inference])\n",
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" Downloading python_magic-0.4.27-py2.py3-none-any.whl (13 kB)\n",
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"Requirement already satisfied: lxml in /usr/local/lib/python3.10/dist-packages (from unstructured[local-inference]) (4.9.4)\n",
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+
"Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (from unstructured[local-inference]) (3.8.1)\n",
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"Requirement already satisfied: tabulate in /usr/local/lib/python3.10/dist-packages (from unstructured[local-inference]) (0.9.0)\n",
|
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+
"Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from unstructured[local-inference]) (4.12.3)\n",
|
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+
"Collecting emoji (from unstructured[local-inference])\n",
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+
" Downloading emoji-2.11.0-py2.py3-none-any.whl (433 kB)\n",
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+
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m433.8/433.8 kB\u001b[0m \u001b[31m29.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hCollecting python-iso639 (from unstructured[local-inference])\n",
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" Downloading python_iso639-2024.2.7-py3-none-any.whl (274 kB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m274.7/274.7 kB\u001b[0m \u001b[31m16.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hCollecting langdetect (from unstructured[local-inference])\n",
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" Created wheel for iopath: filename=iopath-0.1.10-py3-none-any.whl size=31532 sha256=b2aa25855332a5c43eef0c3000cb3b16d8a4c9f2578bc12e110a880bc2523f92\n",
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"Installing collected packages: sseclient-py, filetype, antlr4-python3-runtime, XlsxWriter, rapidfuzz, python-multipart, python-magic, python-iso639, python-docx, pypdfium2, pypdf, pypandoc, portalocker, pillow, packaging, orjson, onnx, omegaconf, olefile, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, mypy-extensions, langdetect, jsonpointer, jsonpath-python, humanfriendly, emoji, Deprecated, backoff, unstructured.pytesseract, typing-inspect, python-pptx, pytesseract, pillow_heif, pikepdf, pdf2image, nvidia-cusparse-cu12, nvidia-cudnn-cu12, msg-parser, marshmallow, jsonpatch, iopath, coloredlogs, together, pdfminer.six, onnxruntime, nvidia-cusolver-cu12, langsmith, dataclasses-json-speakeasy, dataclasses-json, unstructured-client, pdfplumber, langchain-core, unstructured, layoutparser, langchain-together, langchain-text-splitters, langchain_community, timm, effdet, unstructured-inference\n",
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"\u001b[0mSuccessfully installed Deprecated-1.2.14 XlsxWriter-3.2.0 antlr4-python3-runtime-4.9.3 backoff-2.2.1 coloredlogs-15.0.1 dataclasses-json-0.6.4 dataclasses-json-speakeasy-0.5.11 effdet-0.4.1 emoji-2.11.0 filetype-1.2.0 humanfriendly-10.0 iopath-0.1.10 jsonpatch-1.33 jsonpath-python-1.0.6 jsonpointer-2.4 langchain-core-0.1.42 langchain-text-splitters-0.0.1 langchain-together-0.1.0 langchain_community-0.0.32 langdetect-1.0.9 langsmith-0.1.47 layoutparser-0.3.4 marshmallow-3.21.1 msg-parser-1.2.0 mypy-extensions-1.0.0 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 olefile-0.47 omegaconf-2.3.0 onnx-1.16.0 onnxruntime-1.15.1 orjson-3.10.0 packaging-23.2 pdf2image-1.17.0 pdfminer.six-20231228 pdfplumber-0.11.0 pikepdf-8.15.0 pillow-10.3.0 pillow_heif-0.16.0 portalocker-2.8.2 pypandoc-1.13 pypdf-4.2.0 pypdfium2-4.29.0 pytesseract-0.3.10 python-docx-1.1.0 python-iso639-2024.2.7 python-magic-0.4.27 python-multipart-0.0.9 python-pptx-0.6.23 rapidfuzz-3.8.1 sseclient-py-1.8.0 timm-0.9.16 together-0.2.11 typing-inspect-0.9.0 unstructured-0.13.2 unstructured-client-0.18.0 unstructured-inference-0.7.25 unstructured.pytesseract-0.3.12\n"
|
587 |
+
]
|
588 |
+
},
|
589 |
+
{
|
590 |
+
"output_type": "display_data",
|
591 |
+
"data": {
|
592 |
+
"application/vnd.colab-display-data+json": {
|
593 |
+
"pip_warning": {
|
594 |
+
"packages": [
|
595 |
+
"PIL",
|
596 |
+
"pydevd_plugins"
|
597 |
+
]
|
598 |
+
},
|
599 |
+
"id": "a05b30d70de54e25a3d0c32fffc55ab0"
|
600 |
+
}
|
601 |
+
},
|
602 |
+
"metadata": {}
|
603 |
+
}
|
604 |
+
]
|
605 |
+
},
|
606 |
+
{
|
607 |
+
"cell_type": "code",
|
608 |
+
"source": [
|
609 |
+
"from langchain_community.document_loaders import TextLoader\n",
|
610 |
+
"from langchain_community.embeddings.fake import FakeEmbeddings\n",
|
611 |
+
"from langchain_community.vectorstores import Vectara\n",
|
612 |
+
"from langchain_text_splitters import CharacterTextSplitter"
|
613 |
+
],
|
614 |
+
"metadata": {
|
615 |
+
"id": "bSRybIQ60tRl"
|
616 |
+
},
|
617 |
+
"execution_count": 5,
|
618 |
+
"outputs": []
|
619 |
+
},
|
620 |
+
{
|
621 |
+
"cell_type": "code",
|
622 |
+
"source": [
|
623 |
+
"from google.colab import userdata\n",
|
624 |
+
"\n",
|
625 |
+
"TOGETHER_API_KEY = userdata.get('TOGETHER_API_KEY')\n",
|
626 |
+
"vectara_customer_id = userdata.get('VECTARA_CUSTOMER_ID')\n",
|
627 |
+
"vectara_corpus_id = userdata.get('VECTARA_CORPUS_ID')\n",
|
628 |
+
"vectara_api_key = userdata.get('VECTARA_API_KEY')"
|
629 |
+
],
|
630 |
+
"metadata": {
|
631 |
+
"id": "d98hRDFC3WyH"
|
632 |
+
},
|
633 |
+
"execution_count": 6,
|
634 |
+
"outputs": []
|
635 |
+
},
|
636 |
+
{
|
637 |
+
"cell_type": "code",
|
638 |
+
"source": [
|
639 |
+
"vectorstore = Vectara(\n",
|
640 |
+
" vectara_customer_id=vectara_customer_id,\n",
|
641 |
+
" vectara_corpus_id=vectara_corpus_id,\n",
|
642 |
+
" vectara_api_key=vectara_api_key\n",
|
643 |
+
" )"
|
644 |
+
],
|
645 |
+
"metadata": {
|
646 |
+
"id": "n7aGHYcyzgXK"
|
647 |
+
},
|
648 |
+
"execution_count": 7,
|
649 |
+
"outputs": []
|
650 |
+
},
|
651 |
+
{
|
652 |
+
"cell_type": "code",
|
653 |
+
"source": [
|
654 |
+
"from langchain_community.document_loaders import UnstructuredPDFLoader"
|
655 |
+
],
|
656 |
+
"metadata": {
|
657 |
+
"id": "aX5VJiU07RZs"
|
658 |
+
},
|
659 |
+
"execution_count": 8,
|
660 |
+
"outputs": []
|
661 |
+
},
|
662 |
+
{
|
663 |
+
"cell_type": "code",
|
664 |
+
"source": [
|
665 |
+
"!mkdir docs\n",
|
666 |
+
"# upload sample file"
|
667 |
+
],
|
668 |
+
"metadata": {
|
669 |
+
"id": "UQors5XgGPV7"
|
670 |
+
},
|
671 |
+
"execution_count": 37,
|
672 |
+
"outputs": []
|
673 |
+
},
|
674 |
+
{
|
675 |
+
"cell_type": "code",
|
676 |
+
"source": [
|
677 |
+
"loader = UnstructuredPDFLoader('ISB-020-U3-W-S-01-B18003-001-020.pdf', strategy='fast')\n",
|
678 |
+
"data = loader.load()"
|
679 |
+
],
|
680 |
+
"metadata": {
|
681 |
+
"id": "ULSBXZRcI_4R"
|
682 |
+
},
|
683 |
+
"execution_count": 9,
|
684 |
+
"outputs": []
|
685 |
+
},
|
686 |
+
{
|
687 |
+
"cell_type": "code",
|
688 |
+
"source": [
|
689 |
+
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
|
690 |
+
"docs = text_splitter.split_documents(data)"
|
691 |
+
],
|
692 |
+
"metadata": {
|
693 |
+
"colab": {
|
694 |
+
"base_uri": "https://localhost:8080/"
|
695 |
+
},
|
696 |
+
"id": "rd_8GLJrPT5T",
|
697 |
+
"outputId": "002488bd-f8a0-4099-c4c4-b685da7a8195"
|
698 |
+
},
|
699 |
+
"execution_count": 10,
|
700 |
+
"outputs": [
|
701 |
+
{
|
702 |
+
"output_type": "stream",
|
703 |
+
"name": "stderr",
|
704 |
+
"text": [
|
705 |
+
"WARNING:langchain_text_splitters.base:Created a chunk of size 3260, which is longer than the specified 1000\n",
|
706 |
+
"WARNING:langchain_text_splitters.base:Created a chunk of size 1754, which is longer than the specified 1000\n",
|
707 |
+
"WARNING:langchain_text_splitters.base:Created a chunk of size 1556, which is longer than the specified 1000\n",
|
708 |
+
"WARNING:langchain_text_splitters.base:Created a chunk of size 2529, which is longer than the specified 1000\n",
|
709 |
+
"WARNING:langchain_text_splitters.base:Created a chunk of size 2108, which is longer than the specified 1000\n",
|
710 |
+
"WARNING:langchain_text_splitters.base:Created a chunk of size 1240, which is longer than the specified 1000\n",
|
711 |
+
"WARNING:langchain_text_splitters.base:Created a chunk of size 1122, which is longer than the specified 1000\n"
|
712 |
+
]
|
713 |
+
}
|
714 |
+
]
|
715 |
+
},
|
716 |
+
{
|
717 |
+
"cell_type": "code",
|
718 |
+
"source": [
|
719 |
+
"import json\n",
|
720 |
+
"\n",
|
721 |
+
"from langchain_community.document_transformers import DoctranPropertyExtractor\n",
|
722 |
+
"from langchain_core.documents import Document"
|
723 |
+
],
|
724 |
+
"metadata": {
|
725 |
+
"id": "6CM6bL6JRCCA"
|
726 |
+
},
|
727 |
+
"execution_count": 3,
|
728 |
+
"outputs": []
|
729 |
+
},
|
730 |
+
{
|
731 |
+
"cell_type": "code",
|
732 |
+
"source": [
|
733 |
+
"properties = [\n",
|
734 |
+
" {\n",
|
735 |
+
" \"name\": \"document_number\",\n",
|
736 |
+
" \"description\": \"Unique identifier for the document within its project.\",\n",
|
737 |
+
" \"type\": \"string\",\n",
|
738 |
+
" \"required\": True\n",
|
739 |
+
" },\n",
|
740 |
+
" {\n",
|
741 |
+
" \"name\": \"discipline\",\n",
|
742 |
+
" \"description\": \"The discipline associated with the document.\",\n",
|
743 |
+
" \"type\": \"string\",\n",
|
744 |
+
" \"required\": True\n",
|
745 |
+
" },\n",
|
746 |
+
" {\n",
|
747 |
+
" \"name\": \"title\",\n",
|
748 |
+
" \"description\": \"Title of the document.\",\n",
|
749 |
+
" \"type\": \"string\",\n",
|
750 |
+
" \"required\": True\n",
|
751 |
+
" },\n",
|
752 |
+
" {\n",
|
753 |
+
" \"name\": \"version\",\n",
|
754 |
+
" \"description\": \"Version number of the document.\",\n",
|
755 |
+
" \"type\": \"integer\",\n",
|
756 |
+
" \"required\": True\n",
|
757 |
+
" },\n",
|
758 |
+
" {\n",
|
759 |
+
" \"name\": \"date\",\n",
|
760 |
+
" \"description\": \"Creation date of the document.\",\n",
|
761 |
+
" \"type\": \"string\",\n",
|
762 |
+
" \"format\": \"date\",\n",
|
763 |
+
" \"required\": True\n",
|
764 |
+
" },\n",
|
765 |
+
" {\n",
|
766 |
+
" \"name\": \"author\",\n",
|
767 |
+
" \"description\": \"Author of the document.\",\n",
|
768 |
+
" \"type\": \"object\",\n",
|
769 |
+
" \"properties\": {\n",
|
770 |
+
" \"name\": {\n",
|
771 |
+
" \"type\": \"string\",\n",
|
772 |
+
" \"required\": True\n",
|
773 |
+
" },\n",
|
774 |
+
" \"email\": {\n",
|
775 |
+
" \"type\": \"string\",\n",
|
776 |
+
" \"format\": \"email\",\n",
|
777 |
+
" \"required\": False\n",
|
778 |
+
" }\n",
|
779 |
+
" },\n",
|
780 |
+
" \"required\": True\n",
|
781 |
+
" },\n",
|
782 |
+
" {\n",
|
783 |
+
" \"name\": \"related_documents\",\n",
|
784 |
+
" \"description\": \"List of related documents.\",\n",
|
785 |
+
" \"type\": \"array\",\n",
|
786 |
+
" \"items\": {\n",
|
787 |
+
" \"type\": \"string\"\n",
|
788 |
+
" },\n",
|
789 |
+
" \"required\": False\n",
|
790 |
+
" },\n",
|
791 |
+
" {\n",
|
792 |
+
" \"name\": \"status\",\n",
|
793 |
+
" \"description\": \"Current status of the document.\",\n",
|
794 |
+
" \"type\": \"string\",\n",
|
795 |
+
" \"enum\": [\"draft\", \"under_review\", \"approved\", \"rejected\"],\n",
|
796 |
+
" \"required\": True\n",
|
797 |
+
" },\n",
|
798 |
+
" {\n",
|
799 |
+
" \"name\": \"keywords\",\n",
|
800 |
+
" \"description\": \"Keywords associated with the document.\",\n",
|
801 |
+
" \"type\": \"array\",\n",
|
802 |
+
" \"items\": {\n",
|
803 |
+
" \"type\": \"string\"\n",
|
804 |
+
" },\n",
|
805 |
+
" \"required\": False\n",
|
806 |
+
" },\n",
|
807 |
+
" {\n",
|
808 |
+
" \"name\": \"summary\",\n",
|
809 |
+
" \"description\": \"Short summary of the document content.\",\n",
|
810 |
+
" \"type\": \"string\",\n",
|
811 |
+
" \"required\": False\n",
|
812 |
+
" }\n",
|
813 |
+
"]"
|
814 |
+
],
|
815 |
+
"metadata": {
|
816 |
+
"id": "9rBUSiR-bDAD"
|
817 |
+
},
|
818 |
+
"execution_count": 12,
|
819 |
+
"outputs": []
|
820 |
+
},
|
821 |
+
{
|
822 |
+
"cell_type": "code",
|
823 |
+
"source": [
|
824 |
+
"property_extractor = DoctranPropertyExtractor(properties=properties)"
|
825 |
+
],
|
826 |
+
"metadata": {
|
827 |
+
"colab": {
|
828 |
+
"base_uri": "https://localhost:8080/",
|
829 |
+
"height": 339
|
830 |
+
},
|
831 |
+
"id": "H5jIV-OYfJRg",
|
832 |
+
"outputId": "0eb7dc55-088e-4912-c85e-dc4eb87e442c"
|
833 |
+
},
|
834 |
+
"execution_count": 13,
|
835 |
+
"outputs": [
|
836 |
+
{
|
837 |
+
"output_type": "error",
|
838 |
+
"ename": "ValueError",
|
839 |
+
"evalue": "Did not find openai_api_key, please add an environment variable `OPENAI_API_KEY` which contains it, or pass `openai_api_key` as a named parameter.",
|
840 |
+
"traceback": [
|
841 |
+
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
842 |
+
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
|
843 |
+
"\u001b[0;32m<ipython-input-13-2ab54c930860>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mproperty_extractor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDoctranPropertyExtractor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproperties\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mproperties\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
844 |
+
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_community/document_transformers/doctran_text_extract.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, properties, openai_api_key, openai_api_model)\u001b[0m\n\u001b[1;32m 57\u001b[0m ) -> None:\n\u001b[1;32m 58\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproperties\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproperties\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m self.openai_api_key = openai_api_key or get_from_env(\n\u001b[0m\u001b[1;32m 60\u001b[0m \u001b[0;34m\"openai_api_key\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"OPENAI_API_KEY\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 61\u001b[0m )\n",
|
845 |
+
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/utils/env.py\u001b[0m in \u001b[0;36mget_from_env\u001b[0;34m(key, env_key, default)\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdefault\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m raise ValueError(\n\u001b[0m\u001b[1;32m 42\u001b[0m \u001b[0;34mf\"Did not find {key}, please add an environment variable\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0;34mf\" `{env_key}` which contains it, or pass\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
846 |
+
"\u001b[0;31mValueError\u001b[0m: Did not find openai_api_key, please add an environment variable `OPENAI_API_KEY` which contains it, or pass `openai_api_key` as a named parameter."
|
847 |
+
]
|
848 |
+
}
|
849 |
+
]
|
850 |
+
},
|
851 |
+
{
|
852 |
+
"cell_type": "markdown",
|
853 |
+
"source": [],
|
854 |
+
"metadata": {
|
855 |
+
"id": "hVjJAK-KTTEE"
|
856 |
+
}
|
857 |
+
},
|
858 |
+
{
|
859 |
+
"cell_type": "code",
|
860 |
+
"source": [
|
861 |
+
"from dotenv import load_dotenv\n",
|
862 |
+
"\n",
|
863 |
+
"load_dotenv()"
|
864 |
+
],
|
865 |
+
"metadata": {
|
866 |
+
"colab": {
|
867 |
+
"base_uri": "https://localhost:8080/",
|
868 |
+
"height": 349
|
869 |
+
},
|
870 |
+
"id": "HVDCIqIDRJ3Z",
|
871 |
+
"outputId": "4fbd508a-2179-4251-d2ae-2df5cce24187"
|
872 |
+
},
|
873 |
+
"execution_count": 4,
|
874 |
+
"outputs": [
|
875 |
+
{
|
876 |
+
"output_type": "error",
|
877 |
+
"ename": "ModuleNotFoundError",
|
878 |
+
"evalue": "No module named 'dotenv'",
|
879 |
+
"traceback": [
|
880 |
+
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
881 |
+
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
|
882 |
+
"\u001b[0;32m<ipython-input-4-c9bdfc1ba4a4>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mdotenv\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_dotenv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mload_dotenv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
883 |
+
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'dotenv'",
|
884 |
+
"",
|
885 |
+
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
|
886 |
+
],
|
887 |
+
"errorDetails": {
|
888 |
+
"actions": [
|
889 |
+
{
|
890 |
+
"action": "open_url",
|
891 |
+
"actionText": "Open Examples",
|
892 |
+
"url": "/notebooks/snippets/importing_libraries.ipynb"
|
893 |
+
}
|
894 |
+
]
|
895 |
+
}
|
896 |
+
}
|
897 |
+
]
|
898 |
+
}
|
899 |
+
]
|
900 |
+
}
|
langchain_vectara.py
ADDED
@@ -0,0 +1,134 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# -*- coding: utf-8 -*-
|
2 |
+
"""langchain_vectara.ipynb
|
3 |
+
|
4 |
+
Automatically generated by Colab.
|
5 |
+
|
6 |
+
Original file is located at
|
7 |
+
https://colab.research.google.com/drive/1XzD7XHO_a-gYFBnGUWH1MOtstT4sDY3J
|
8 |
+
"""
|
9 |
+
|
10 |
+
!pip install -r requirements.txt
|
11 |
+
|
12 |
+
!pip install langchain_community langchain-text-splitters unstructured[local-inference] pdf2image pdfminer.six langchain-together pillow_heif
|
13 |
+
|
14 |
+
from langchain_community.document_loaders import TextLoader
|
15 |
+
from langchain_community.embeddings.fake import FakeEmbeddings
|
16 |
+
from langchain_community.vectorstores import Vectara
|
17 |
+
from langchain_text_splitters import CharacterTextSplitter
|
18 |
+
|
19 |
+
from google.colab import userdata
|
20 |
+
|
21 |
+
TOGETHER_API_KEY = userdata.get('TOGETHER_API_KEY')
|
22 |
+
vectara_customer_id = userdata.get('VECTARA_CUSTOMER_ID')
|
23 |
+
vectara_corpus_id = userdata.get('VECTARA_CORPUS_ID')
|
24 |
+
vectara_api_key = userdata.get('VECTARA_API_KEY')
|
25 |
+
|
26 |
+
vectorstore = Vectara(
|
27 |
+
vectara_customer_id=vectara_customer_id,
|
28 |
+
vectara_corpus_id=vectara_corpus_id,
|
29 |
+
vectara_api_key=vectara_api_key
|
30 |
+
)
|
31 |
+
|
32 |
+
from langchain_community.document_loaders import UnstructuredPDFLoader
|
33 |
+
|
34 |
+
!mkdir docs
|
35 |
+
# upload sample file
|
36 |
+
|
37 |
+
loader = UnstructuredPDFLoader('ISB-020-U3-W-S-01-B18003-001-020.pdf', strategy='fast')
|
38 |
+
data = loader.load()
|
39 |
+
|
40 |
+
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
|
41 |
+
docs = text_splitter.split_documents(data)
|
42 |
+
|
43 |
+
import json
|
44 |
+
|
45 |
+
from langchain_community.document_transformers import DoctranPropertyExtractor
|
46 |
+
from langchain_core.documents import Document
|
47 |
+
|
48 |
+
properties = [
|
49 |
+
{
|
50 |
+
"name": "document_number",
|
51 |
+
"description": "Unique identifier for the document within its project.",
|
52 |
+
"type": "string",
|
53 |
+
"required": True
|
54 |
+
},
|
55 |
+
{
|
56 |
+
"name": "discipline",
|
57 |
+
"description": "The discipline associated with the document.",
|
58 |
+
"type": "string",
|
59 |
+
"required": True
|
60 |
+
},
|
61 |
+
{
|
62 |
+
"name": "title",
|
63 |
+
"description": "Title of the document.",
|
64 |
+
"type": "string",
|
65 |
+
"required": True
|
66 |
+
},
|
67 |
+
{
|
68 |
+
"name": "version",
|
69 |
+
"description": "Version number of the document.",
|
70 |
+
"type": "integer",
|
71 |
+
"required": True
|
72 |
+
},
|
73 |
+
{
|
74 |
+
"name": "date",
|
75 |
+
"description": "Creation date of the document.",
|
76 |
+
"type": "string",
|
77 |
+
"format": "date",
|
78 |
+
"required": True
|
79 |
+
},
|
80 |
+
{
|
81 |
+
"name": "author",
|
82 |
+
"description": "Author of the document.",
|
83 |
+
"type": "object",
|
84 |
+
"properties": {
|
85 |
+
"name": {
|
86 |
+
"type": "string",
|
87 |
+
"required": True
|
88 |
+
},
|
89 |
+
"email": {
|
90 |
+
"type": "string",
|
91 |
+
"format": "email",
|
92 |
+
"required": False
|
93 |
+
}
|
94 |
+
},
|
95 |
+
"required": True
|
96 |
+
},
|
97 |
+
{
|
98 |
+
"name": "related_documents",
|
99 |
+
"description": "List of related documents.",
|
100 |
+
"type": "array",
|
101 |
+
"items": {
|
102 |
+
"type": "string"
|
103 |
+
},
|
104 |
+
"required": False
|
105 |
+
},
|
106 |
+
{
|
107 |
+
"name": "status",
|
108 |
+
"description": "Current status of the document.",
|
109 |
+
"type": "string",
|
110 |
+
"enum": ["draft", "under_review", "approved", "rejected"],
|
111 |
+
"required": True
|
112 |
+
},
|
113 |
+
{
|
114 |
+
"name": "keywords",
|
115 |
+
"description": "Keywords associated with the document.",
|
116 |
+
"type": "array",
|
117 |
+
"items": {
|
118 |
+
"type": "string"
|
119 |
+
},
|
120 |
+
"required": False
|
121 |
+
},
|
122 |
+
{
|
123 |
+
"name": "summary",
|
124 |
+
"description": "Short summary of the document content.",
|
125 |
+
"type": "string",
|
126 |
+
"required": False
|
127 |
+
}
|
128 |
+
]
|
129 |
+
|
130 |
+
property_extractor = DoctranPropertyExtractor(properties=properties)
|
131 |
+
|
132 |
+
from dotenv import load_dotenv
|
133 |
+
|
134 |
+
load_dotenv()
|
requirements.txt
CHANGED
@@ -1 +1,9 @@
|
|
1 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
langchain_community
|
2 |
+
langchain-text-splitters
|
3 |
+
langchain-together
|
4 |
+
unstructured[local-inference]
|
5 |
+
pdf2image
|
6 |
+
pdfminer.six
|
7 |
+
pillow_heif
|
8 |
+
doctran
|
9 |
+
python-dotenv
|
schema.py
ADDED
@@ -0,0 +1,130 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
import json
|
3 |
+
from typing import List, Optional
|
4 |
+
from pydantic import BaseModel, Field
|
5 |
+
from enum import Enum
|
6 |
+
|
7 |
+
class BimDiscipline(Enum):
|
8 |
+
plumbing = 'S - Sanitär'
|
9 |
+
network = 'd - Datennetz'
|
10 |
+
heating = 'h - Heizung'
|
11 |
+
electrical = 'e - Elektro'
|
12 |
+
ventilation = 'l - Lüftung'
|
13 |
+
architecture = 'a - Architektur'
|
14 |
+
|
15 |
+
class KeywordChoice(str, Enum):
|
16 |
+
three_dimensional_modeling = "3d modeling"
|
17 |
+
four_dimensional_simulation = "4d simulation"
|
18 |
+
five_dimensional_cost_estimation = "5d cost estimation"
|
19 |
+
six_dimensional_lifecycle_management = "6d lifecycle management"
|
20 |
+
adoption = "adoptions"
|
21 |
+
agile_bim = "agile bim"
|
22 |
+
artificial_intelligence = "artificial intelligence"
|
23 |
+
automated_code_checking = "automated code checking"
|
24 |
+
automation = "automation"
|
25 |
+
big_data_analytics = "big data analytics"
|
26 |
+
bim_based_estimating = "bim-based estimating"
|
27 |
+
bim_execution_plan = "bim execution plan"
|
28 |
+
bim_governance = "bim governance"
|
29 |
+
bim_integration = "bim integration"
|
30 |
+
bim_maturity = "bim maturity"
|
31 |
+
bim_objects = "bim objects"
|
32 |
+
bim_process = "bim process"
|
33 |
+
bim_quality_assurance = "bim quality assurance"
|
34 |
+
bim_requirements = "bim requirements"
|
35 |
+
bim_server = "bim server"
|
36 |
+
bim_standardization = "bim standardization"
|
37 |
+
bim_strategy = "bim strategy"
|
38 |
+
bim_tools = "bim tools"
|
39 |
+
bim_training = "bim training"
|
40 |
+
barriers = "barriers"
|
41 |
+
benchmarking = "benchmarking"
|
42 |
+
building_energy_modeling = "building energy modeling"
|
43 |
+
building_life_cycle = "building life cycle"
|
44 |
+
building_physics = "building physics"
|
45 |
+
building_smartness = "building smartness"
|
46 |
+
built_environment = "built environment"
|
47 |
+
case_studies = "case studies"
|
48 |
+
cloud_computing = "cloud computing"
|
49 |
+
collaborative_environments = "collaborative environments"
|
50 |
+
communication = "communication"
|
51 |
+
computational_design = "computational design"
|
52 |
+
cobie = "construction operations building information exchange"
|
53 |
+
construction_scheduling = "construction scheduling"
|
54 |
+
construction_site_layout = "construction site layout"
|
55 |
+
context_awareness = "context awareness"
|
56 |
+
contractual_issues = "contractual issues"
|
57 |
+
cost_control = "cost control"
|
58 |
+
critical_success_factors = "critical success factors"
|
59 |
+
digital_fabrication = "digital fabrication"
|
60 |
+
digital_twin = "digital twin"
|
61 |
+
disaster_response = "disaster response"
|
62 |
+
dispute_resolution = "dispute resolution"
|
63 |
+
dynamic_scheduling = "dynamic scheduling"
|
64 |
+
education = "education"
|
65 |
+
electronic_tendering = "electronic tendering"
|
66 |
+
empirical_research = "empirical research"
|
67 |
+
energy_analysis = "energy analysis"
|
68 |
+
environmental_impacts = "environmental impacts"
|
69 |
+
facility_management = "facility management"
|
70 |
+
fire_safety = "fire safety"
|
71 |
+
gis_integration = "gis integration"
|
72 |
+
green_buildings = "green buildings"
|
73 |
+
health_and_safety = "health and safety"
|
74 |
+
historical_preservation = "historical preservation"
|
75 |
+
human_behavior = "human behavior"
|
76 |
+
industrialized_construction = "industrialized construction"
|
77 |
+
information_management = "information management"
|
78 |
+
infrastructure_asset_management = "infrastructure asset management"
|
79 |
+
interoperability = "interoperability"
|
80 |
+
iot_integration = "iot integration"
|
81 |
+
knowledge_sharing = "knowledge sharing"
|
82 |
+
lean_construction = "lean construction"
|
83 |
+
legal_frameworks = "legal frameworks"
|
84 |
+
level_of_development = "level of development"
|
85 |
+
level_of_detail = "level of detail"
|
86 |
+
level_of_information_need = "level of information need"
|
87 |
+
lifetime_costs = "lifetime costs"
|
88 |
+
linked_data = "linked data"
|
89 |
+
machine_learning = "machine learning"
|
90 |
+
manufacturing = "manufacturing"
|
91 |
+
materials_management = "materials management"
|
92 |
+
mobile_applications = "mobile applications"
|
93 |
+
multi_criteria_decision_making = "multi-criteria decision making"
|
94 |
+
natural_language_processing = "natural language processing"
|
95 |
+
networking = "networking"
|
96 |
+
open_bim = "open bim"
|
97 |
+
organizational_change = "organizational change"
|
98 |
+
owner_involvement = "owner involvement"
|
99 |
+
parametric_design = "parametric design"
|
100 |
+
performance_measurement = "performance measurement"
|
101 |
+
pipeline_simulation = "pipeline simulation"
|
102 |
+
policy_implications = "policy implications"
|
103 |
+
predictive_maintenance = "predictive maintenance"
|
104 |
+
product_data_templates = "product data templates"
|
105 |
+
public_procurement = "public procurement"
|
106 |
+
radio_frequency_identification = "radio frequency identification"
|
107 |
+
reality_capture = "reality capture"
|
108 |
+
real_time_monitoring = "real-time monitoring"
|
109 |
+
reliability_engineering = "reliability engineering"
|
110 |
+
resilience = "resilience"
|
111 |
+
return_on_investment = "return on investment"
|
112 |
+
reverse_logistics = "reverse logistics"
|
113 |
+
risk_assessment = "risk assessment"
|
114 |
+
robotics = "robotics"
|
115 |
+
maintenance_management = "maintenance management"
|
116 |
+
|
117 |
+
# Define the schema for the output.
|
118 |
+
class Metadata(BaseModel):
|
119 |
+
title: str = Field(description='Title of the document')
|
120 |
+
summary: str = Field(description='One sentence short summary of the document information')
|
121 |
+
disciplines: List[str] = Field(
|
122 |
+
default_factory=list,
|
123 |
+
description='BIM disciplines the document belongs to',
|
124 |
+
example=['S - Sanitär', 'D - Datennetz']
|
125 |
+
)
|
126 |
+
keywords: List[str] = Field(
|
127 |
+
default_factory=list,
|
128 |
+
description='List of BIM keywords associated with the document',
|
129 |
+
example=['3D Modelling', 'Clash detection']
|
130 |
+
)
|
together_call.py
ADDED
@@ -0,0 +1,44 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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import os
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import json
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import openai
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from pydantic import BaseModel, Field
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from dotenv import load_dotenv
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load_dotenv()
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# Create client
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client = openai.OpenAI(
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base_url="https://api.together.xyz/v1",
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api_key=os.environ["TOGETHER_API_KEY"],
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)
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# Define the schema for the output.
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class User(BaseModel):
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name: str = Field(description="user name")
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address: str = Field(description="address")
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# Call the LLM with the JSON schema
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chat_completion = client.chat.completions.create(
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model="mistralai/Mixtral-8x7B-Instruct-v0.1",
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response_format={"type": "json_object", "schema": User.model_json_schema()},
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messages=[
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{
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"role": "system",
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"content": "You are a helpful assistant that answers in JSON.",
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},
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{
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"role": "user",
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"content": "Create a user named Alice, who lives in 42, Wonderland Avenue.",
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},
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],
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)
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created_user = json.loads(chat_completion.choices[0].message.content)
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print(json.dumps(created_user, indent=2))
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"""
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{
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"address": "42, Wonderland Avenue",
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"name": "Alice"
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}
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"""
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