lookup

Example:

from langchain_openai import OpenAIEmbeddings
cache = RedisSemanticCache(
    embeddings=OpenAIEmbeddings(),
    redis_url="redis://localhost:6379"
)
prompt = "What's the capital city of France?"
llm_string = "openai/gpt-3.5-turbo"

result = cache.lookup(prompt, llm_string)
if result:
    print("Semantic cache hit:", result[0].text)
else:
    print("Semantic cache miss")

Note:

• This method uses vector similarity search to find semantically similar prompts.
• The prompt is embedded using the provided embedding function.
• The method checks for cached results within the distance threshold specified during cache initialization.
• If multiple results are within the threshold, the most similar one is returned.
• The llm_string is used to ensure the cached result is from the same language model.
• This method is typically called internally by LangChain, but can be used directly for manual cache interactions.
• Unlike exact matching, this may return results for prompts that are semantically similar but not identical to the input.