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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>
2.7 KiB
2.7 KiB
In [1]:
from langchain import OpenAI, SQLDatabase, SQLDatabaseChainIn [2]:
db = SQLDatabase.from_uri("sqlite:///../../../../notebooks/Chinook.db")
llm = OpenAI(temperature=0)
db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)In [3]:
db_chain.run("How many employees are there?")Out [3]:
[1m> Entering new SQLDatabaseChain chain...[0m How many employees are there? SQLQuery:[32;1m[1;3m SELECT COUNT(*) FROM Employee;[0m SQLResult: [33;1m[1;3m[(9,)][0m Answer:[32;1m[1;3m There are 9 employees.[0m [1m> Finished SQLDatabaseChain chain.[0m
' There are 9 employees.'
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