entity

Chain to run queries against LLMs.

This class is deprecated. See below for an example implementation using LangChain runnables:

from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_openai import OpenAI

prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(input_variables=["adjective"], template=prompt_template)
model = OpenAI()
chain = prompt | model | StrOutputParser()

chain.invoke("your adjective here")

Abstract base class for chat memory.

ATTENTION This abstraction was created prior to when chat models had native tool calling capabilities. It does NOT support native tool calling capabilities for chat models and will fail SILENTLY if used with a chat model that has native tool calling.

DO NOT USE THIS ABSTRACTION FOR NEW CODE.

Abstract base class for Entity store.

In-memory Entity store.

Upstash Redis backed Entity store.

Entities get a TTL of 1 day by default, and that TTL is extended by 3 days every time the entity is read back.

Redis-backed Entity store.

Entities get a TTL of 1 day by default, and that TTL is extended by 3 days every time the entity is read back.

SQLite-backed Entity store with safe query construction.

Entity extractor & summarizer memory.

Extracts named entities from the recent chat history and generates summaries. With a swappable entity store, persisting entities across conversations. Defaults to an in-memory entity store, and can be swapped out for a Redis, SQLite, or other entity store.