Adding implementations for vector stores #79
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AmoghTantradi
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AmoghTantradi
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…mentation Also note that we need to now include deleting previous collections and restarting new ones / error handling for the dimension-mismatch case
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| K = min(K, cur_min) | ||
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| search_K = K | ||
| while True: |
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We should no longer be manually post filtering for the non-faiss vector stores -- instead we should perform a filtered vector search using the VS API (most of them let you filter on an exact match predicate)
| pd.DataFrame: The DataFrame with the index directory saved. | ||
| """ | ||
| if lotus.settings.rm is None: | ||
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it may be good to display a warning here, telling the user to not reset the df index, getting embedding ids requires the df index is invariant so that get_vectors_from_index returns correctly
| metadatas: list[Mapping[str, Union[str, int, float, bool]]] = [{"doc_id": int(i)} for i in range(len(docs_list))] | ||
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| # Add documents in batches | ||
| batch_size = 100 |
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better to add a vs_upsert_batch_size setting that can be configured in our settings module, rather than hard-coding it
| @abstractmethod | ||
| def search(self, | ||
| queries: pd.Series | str | Image.Image | list | NDArray[np.float64], | ||
| def __call__(self, |
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it might make sense to add an ids parameter here, so that sem ops can make filtered search calls easily rather than re-implementing vs-specific logic each time
| }) | ||
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| # Upsert in batches of 100 | ||
| batch_size = 100 |
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move batch-size to a settings configuration (same comment for all vs implementations)
| rm_output: RMOutput = rm(queries, K) | ||
| distances = rm_output.distances | ||
| indices = rm_output.indices | ||
| vs_output: RMOutput = vs(query_vectors, K) |
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we need to add a parameter for ids in vs() and set it to the index of the df we're searching over -- otherwise we won't catch the case when the df being searched over has been filtered by the user
| # VS Only Tests | ||
| ################################################################################ | ||
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an important test to add is doing filtered vector search -- ie the program starts with some df, embeds/indexes the column, does any filter op (can be a structured filter), then calls a sem op that uses search over the indexed column
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Verified tests pass locally (for those that can be run locally at least)