lookup

Example:

.. code-block:: python

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.