Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://python.langchain.com/docs/integrations/providers/spark/ below:

Spark | 🦜️🔗 LangChain

Our new LangChain Academy Course Deep Research with LangGraph is now live!

.

Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.

It loads data from a PySpark DataFrame.

See a usage example.

from langchain_community.document_loaders import PySparkDataFrameLoader

Toolkit for interacting with Spark SQL.

See a usage example.

from langchain_community.agent_toolkits import SparkSQLToolkit, create_spark_sql_agent
from langchain_community.utilities.spark_sql import SparkSQL

You can use individual tools from the Spark SQL Toolkit:

• InfoSparkSQLTool: tool for getting metadata about a Spark SQL
• ListSparkSQLTool: tool for getting tables names
• QueryCheckerTool: tool uses an LLM to check if a query is correct
• QuerySparkSQLTool: tool for querying a Spark SQL

from langchain_community.tools.spark_sql.tool import InfoSparkSQLTool
from langchain_community.tools.spark_sql.tool import ListSparkSQLTool
from langchain_community.tools.spark_sql.tool import QueryCheckerTool
from langchain_community.tools.spark_sql.tool import QuerySparkSQLTool

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4