Construct a Power BI agent from a Chat LLM and tools.
agent_toolkits.spark_sql.base.create_spark_sql_agent(...)
Construct a Spark SQL agent from an LLM and tools.
agent_toolkits.sql.base.create_sql_agent(...)
Construct an SQL agent from an LLM and tools.
langchain_community.cache¶
Warning
Beta Feature!
Cache provides an optional caching layer for LLMs.
Cache is useful for two reasons:
It can save you money by reducing the number of API calls you make to the LLM
provider if you’re often requesting the same completion multiple times.
It can speed up your application by reducing the number of API calls you make
to the LLM provider.
Cache directly competes with Memory. See documentation for Pros and Cons.
Class hierarchy:
BaseCache --> <name>Cache  # Examples: InMemoryCache, RedisCache, GPTCache
Classes¶
cache.AstraDBCache(*[, collection_name, ...])
Cache that uses Astra DB as a backend.
cache.AstraDBSemanticCache(*[, ...])
Cache that uses Astra DB as a vector-store backend for semantic (i.e.
cache.CassandraCache([session, keyspace, ...])
Cache that uses Cassandra / Astra DB as a backend.
cache.CassandraSemanticCache(session, ...[, ...])
Cache that uses Cassandra as a vector-store backend for semantic (i.e.
cache.FullLLMCache(**kwargs)
SQLite table for full LLM Cache (all generations).
cache.FullMd5LLMCache(**kwargs)
SQLite table for full LLM Cache (all generations).
cache.GPTCache([init_func])
Cache that uses GPTCache as a backend.
cache.InMemoryCache()
Cache that stores things in memory.
cache.MomentoCache(cache_client, cache_name, *)