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actions-user committed Oct 28, 2023
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48 changes: 24 additions & 24 deletions .github/push_templates_to_algolia.ipynb
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104 changes: 81 additions & 23 deletions LangChain/LangChain_Perform_QA_on_SQL_Databases.ipynb
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"tags": []
},
"source": [
"# Langchain - QA on SQL Database"
"# LangChain - QA on SQL Database\n",
"<a href=\"https://app.naas.ai/user-redirect/naas/downloader?url=https://raw.githubusercontent.com/jupyter-naas/awesome-notebooks/master/LangChain/LangChain_Perform_QA_on_SQL_Databases.ipynb\" target=\"_parent\"><img src=\"https://naasai-public.s3.eu-west-3.amazonaws.com/Open_in_Naas_Lab.svg\"/></a><br><br><a href=\"https://bit.ly/3JyWIk6\">Give Feedback</a> | <a href=\"https://github.com/jupyter-naas/awesome-notebooks/issues/new?assignees=&labels=bug&template=bug_report.md&title=LangChain+-+QA+on+SQL+Database:+Error+short+description\">Bug report</a>"
]
},
{
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{
"cell_type": "markdown",
"id": "2f7ab5dc",
"metadata": {},
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"#### Creating a mock database (Optional)\n",
"The Chinook database is a sample database for you to run quick tests, such as trying out this notebook. It represents a digital media store, including tables for artists, albums, media tracks, invoices and customers.\n",
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{
"cell_type": "markdown",
"id": "3ba70fc1",
"metadata": {},
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"## *Text to SQL query*"
]
},
{
"cell_type": "markdown",
"id": "9b1db6dd",
"metadata": {},
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"## Model"
]
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"cell_type": "code",
"execution_count": 4,
"id": "86ba23e8",
"metadata": {},
"metadata": {
"papermill": {},
"tags": []
},
"outputs": [],
"source": [
"from langchain.chat_models import ChatOpenAI\n",
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{
"cell_type": "markdown",
"id": "4dbca1bc",
"metadata": {},
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"#### Build an SQL query using Create_sql_query_chain"
]
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"cell_type": "code",
"execution_count": 5,
"id": "f66d1a9b",
"metadata": {},
"metadata": {
"papermill": {},
"tags": []
},
"outputs": [],
"source": [
"db = SQLDatabase.from_uri(sql_uri) \n",
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{
"cell_type": "markdown",
"id": "4443a2ae",
"metadata": {},
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"## Output"
]
},
{
"cell_type": "markdown",
"id": "e1eecee9",
"metadata": {},
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"#### Display result"
]
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"cell_type": "code",
"execution_count": 6,
"id": "c3e94abb",
"metadata": {},
"metadata": {
"papermill": {},
"tags": []
},
"outputs": [],
"source": [
"response = chain.invoke({\"question\":\"How many songs are in the Grunge playlist ?\"})\n",
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{
"cell_type": "markdown",
"id": "ebfef2e5",
"metadata": {},
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"You can test out the SQL query by running :"
]
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"cell_type": "code",
"execution_count": null,
"id": "cfb1d20e",
"metadata": {},
"metadata": {
"papermill": {},
"tags": []
},
"outputs": [],
"source": [
"db.run(response)"
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{
"cell_type": "markdown",
"id": "a4923704",
"metadata": {},
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"## *Text to SQL query and execution*"
]
},
{
"cell_type": "markdown",
"id": "4abf9e57",
"metadata": {},
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"## Model"
]
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"cell_type": "code",
"execution_count": 7,
"id": "df536f67",
"metadata": {},
"metadata": {
"papermill": {},
"tags": []
},
"outputs": [],
"source": [
"from langchain_experimental.sql import SQLDatabaseChain"
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{
"cell_type": "markdown",
"id": "3caef398",
"metadata": {},
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"#### Build and run an SQL query using SQLDatabaseChain "
]
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"cell_type": "code",
"execution_count": 8,
"id": "9cc14e45",
"metadata": {},
"metadata": {
"papermill": {},
"tags": []
},
"outputs": [],
"source": [
"db = SQLDatabase.from_uri(sql_uri) \n",
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{
"cell_type": "markdown",
"id": "20a4e159",
"metadata": {},
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"## Output"
]
},
{
"cell_type": "markdown",
"id": "9ea4deda",
"metadata": {},
"metadata": {
"papermill": {},
"tags": []
},
"source": [
"#### Display result"
]
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"cell_type": "code",
"execution_count": 9,
"id": "eb795f63",
"metadata": {},
"metadata": {
"papermill": {},
"tags": []
},
"outputs": [],
"source": [
"db_chain.run(\"Who is the artist with the most albums ?\")"
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"version": "3.11.3"
},
"naas": {
"notebook_id": "658a8244b01dfe532c4faaf8423ce808aca1020f8afdf491d99810b8e1cb4ba1",
"notebook_path": "LangChain/LangChain_CSV_Agent.ipynb"
"notebook_id": "c8dbc30b8e01ffed4b0db7efe88f7120222b35f7b081e86aca522dbe1b294818",
"notebook_path": "LangChain/LangChain_Perform_QA_on_SQL_Databases.ipynb"
},
"papermill": {
"default_parameters": {},
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},
"nbformat": 4,
"nbformat_minor": 5
}
}
1 change: 1 addition & 0 deletions README.md
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* [Gmail Toolkit](https://github.com/jupyter-naas/awesome-notebooks/blob/master/LangChain/LangChain_Gmail_Toolkit.ipynb)
* [JSON Agent](https://github.com/jupyter-naas/awesome-notebooks/blob/master/LangChain/LangChain_JSON_Agent.ipynb)
* [Pandas Dataframe Agent](https://github.com/jupyter-naas/awesome-notebooks/blob/master/LangChain/LangChain_Pandas_Dataframe_Agent.ipynb)
* [QA on SQL Database](https://github.com/jupyter-naas/awesome-notebooks/blob/master/LangChain/LangChain_Perform_QA_on_SQL_Databases.ipynb)
* [Vector Search on PDF](https://github.com/jupyter-naas/awesome-notebooks/blob/master/LangChain/LangChain_Vector_Search_on_PDF.ipynb)

## LeFigaro
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