meltanolabs / map-gpt-embeddings Goto Github PK
View Code? Open in Web Editor NEWA Singer tap to build vectorized datasets from any knowledge base,using natural language processing (NLP).
License: MIT License
A Singer tap to build vectorized datasets from any knowledge base,using natural language processing (NLP).
License: MIT License
Initially was map-openai-doc-splitter
and map-openai-embeddings
. The second of these two can perform both functions, so it probably makes sense to just combine them and have a config option to enable/disable the embeddings operations.
Technically, the langchain
module is used for doc splitting, not openai
.
While the embeddings do go through OpenAI library directly, the langchain
libraries also have methods to wrap this process.
Options considered:
map-langchain
map-langchain-transforms
map-langchain-embeddings
map-chatgpt-transforms
map-chatgpt-prep
map-chatgpt-embeddings
map-llm-transforms
map-llm-embeddings
map-llm-document-prep
Today the mapper will split the record text into sub records https://github.com/MeltanoLabs/map-gpt-embeddings/blob/2155fec353868ef7b6334a8c88a3895130440272/map_gpt_embeddings/mappers.py#L129C13-L129C25 if its too big to be embedded on its own. This means a record that enters the mapper with ID = 1 could exit the mapper as 2 records both with ID = 1. I suspect this has downstream implications if the target is updating based on ID, the second record would win and the first would be clobbered.
We could keep the split record index and append it to the key_properties so that the output is a composite PK. For example:
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