Update token usage.
llms.symblai_nebula.completion_with_retry(...)
Use tenacity to retry the completion call.
llms.symblai_nebula.make_request(self, prompt)
Generate text from the model.
llms.tongyi.generate_with_retry(llm, **kwargs)
Use tenacity to retry the completion call.
llms.tongyi.stream_generate_with_retry(llm, ...)
Use tenacity to retry the completion call.
llms.utils.enforce_stop_tokens(text, stop)
Cut off the text as soon as any stop words occur.
llms.vertexai.acompletion_with_retry(llm, prompt)
Use tenacity to retry the completion call.
llms.vertexai.completion_with_retry(llm, prompt)
Use tenacity to retry the completion call.
llms.vertexai.is_codey_model(model_name)
Returns True if the model name is a Codey model.
llms.vertexai.is_gemini_model(model_name)
Returns True if the model name is a Gemini model.
langchain_community.retrievers¶
Retriever class returns Documents given a text query.
It is more general than a vector store. A retriever does not need to be able to
store documents, only to return (or retrieve) it. Vector stores can be used as
the backbone of a retriever, but there are other types of retrievers as well.
Class hierarchy:
BaseRetriever --> <name>Retriever  # Examples: ArxivRetriever, MergerRetriever
Main helpers:
Document, Serializable, Callbacks,
CallbackManagerForRetrieverRun, AsyncCallbackManagerForRetrieverRun
Classes¶
retrievers.arcee.ArceeRetriever
Document retriever for Arcee's Domain Adapted Language Models (DALMs).