Long-Context Retrieval Models with Monarch Mixer
hazyresearch.stanford.edu
Long-Context Retrieval Models with Monarch Mixer
We could have retrieved embeddings for long documents – instead, we’ve retrieved the vectors only for each single word.
Ben Auffarth • Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs
- Multiple indices. Splitting the document corpus up into multiple indices and then routing queries based on some criteria. This means that the search is over a much smaller set of documents rather than the entire dataset. Again, it is not always useful, but it can be helpful for certain datasets. The same approach works with the LLMs themselves.
- Cu
Matt Rickard • Improving RAG: Strategies
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# Definition of used LLM
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def graphPrompt(input: str, metadata={}, model="mixtral:latest"):
if model == None:
model = "mixtral:latest"
chunk_id = metadata.get('chunk_id', None)
# model_info = client.show(model_name=m... See more
# Definition of used LLM
#################################
##########################################################################
def graphPrompt(input: str, metadata={}, model="mixtral:latest"):
if model == None:
model = "mixtral:latest"
chunk_id = metadata.get('chunk_id', None)
# model_info = client.show(model_name=m... See more
Knowledge Graph Extraction & Visualization with local LLM from Unstructured Text: a History example
Why Chat With PDF Is Hard And How ChatLLM Gets It Right
Chatting on long docs is hard because most LLMs other than Gemini don't have a large context.
However, even with Gemini's 1M context length, in-context learning is hard, and if you stuff the doc in the context, it doesn't do a good job.... See more
Bindu Reddyx.com