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https://github.com/hwchase17/langchain.git
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985496f4be
Big docs refactor! Motivation is to make it easier for people to find resources they are looking for. To accomplish this, there are now three main sections: - Getting Started: steps for getting started, walking through most core functionality - Modules: these are different modules of functionality that langchain provides. Each part here has a "getting started", "how to", "key concepts" and "reference" section (except in a few select cases where it didnt easily fit). - Use Cases: this is to separate use cases (like summarization, question answering, evaluation, etc) from the modules, and provide a different entry point to the code base. There is also a full reference section, as well as extra resources (glossary, gallery, etc) Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
130 lines
2.7 KiB
Plaintext
130 lines
2.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "0ed6aab1",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"# SQLite example\n",
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"\n",
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"This example showcases hooking up an LLM to answer questions over a database."
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]
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},
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{
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"cell_type": "markdown",
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"id": "b2f66479",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"This uses the example Chinook database.\n",
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"To set it up follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the `.db` file in a notebooks folder at the root of this repository."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "d0e27d88",
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"from langchain import OpenAI, SQLDatabase, SQLDatabaseChain"
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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": 2,
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"id": "72ede462",
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"db = SQLDatabase.from_uri(\"sqlite:///../../../../notebooks/Chinook.db\")\n",
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"llm = OpenAI(temperature=0)\n",
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"db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=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": 3,
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"id": "15ff81df",
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
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"How many employees are there? \n",
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"SQLQuery:\u001b[32;1m\u001b[1;3m SELECT COUNT(*) FROM Employee;\u001b[0m\n",
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"SQLResult: \u001b[33;1m\u001b[1;3m[(9,)]\u001b[0m\n",
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"Answer:\u001b[32;1m\u001b[1;3m There are 9 employees.\u001b[0m\n",
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"\u001b[1m> Finished SQLDatabaseChain chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"' There are 9 employees.'"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"db_chain.run(\"How many employees are there?\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "61d91b85",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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