update

Example:

from langchain_core.outputs import Generation
from langchain_openai import OpenAIEmbeddings

cache = RedisSemanticCache(
    embeddings=OpenAIEmbeddings(),
    redis_url="redis://localhost:6379"
)
prompt = "What is the capital of France?"
llm_string = "openai/gpt-3.5-turbo"
result = [Generation(text="The capital of France is Paris.")]

cache.update(prompt, llm_string, result)

Note:

• The method uses the provided embedding function to convert the prompt into a vector.
• The vector, along with the prompt, llm_string, and result, is stored in the Redis cache.
• If a TTL (Time To Live) was specified when initializing the cache, it will be applied to this entry.
• This method is typically called internally by LangChain after a language model generates a response, but it can be used directly for manual cache updates.
• Unlike exact matching caches, this allows for semantic similarity lookups later.
• If the cache already contains very similar entries, this will add a new entry rather than overwriting.
• The effectiveness of the cache depends on the quality of the embedding function used.