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core[patch]: Expand documentation in the indexing namespace (#23134)

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Eugene Yurtsev 2024-06-19 10:11:44 -04:00 committed by GitHub
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2 changed files with 45 additions and 5 deletions
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10
libs/core/langchain_core/indexing/api.py
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@ -214,10 +214,18 @@ def index(
are not able to specify the uid of the document.
IMPORTANT:
if auto_cleanup is set to True, the loader should be returning
* if auto_cleanup is set to True, the loader should be returning
the entire dataset, and not just a subset of the dataset.
Otherwise, the auto_cleanup will remove documents that it is not
supposed to.
* In incremental mode, if documents associated with a particular
source id appear across different batches, the indexing API
will do some redundant work. This will still result in the
correct end state of the index, but will unfortunately not be
100% efficient. For example, if a given document is split into 15
chunks, and we index them using a batch size of 5, we'll have 3 batches
all with the same source id. In general, to avoid doing too much
redundant work select as big a batch size as possible.
Args:
docs_source: Data loader or iterable of documents to index.

34
libs/core/langchain_core/indexing/base.py
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@ -5,7 +5,39 @@ from typing import List, Optional, Sequence
class RecordManager(ABC):
"""Abstract base class representing the interface for a record manager."""
"""Abstract base class representing the interface for a record manager.
The record manager abstraction is used by the langchain indexing API.
The record manager keeps track of which documents have been
written into a vectorstore and when they were written.
The indexing API computes hashes for each document and stores the hash
together with the write time and the source id in the record manager.
On subsequent indexing runs, the indexing API can check the record manager
to determine which documents have already been indexed and which have not.
This allows the indexing API to avoid re-indexing documents that have
already been indexed, and to only index new documents.
The main benefit of this abstraction is that it works across many vectorstores.
To be supported, a vectorstore needs to only support the ability to add and
delete documents by ID. Using the record manager, the indexing API will
be able to delete outdated documents and avoid redundant indexing of documents
that have already been indexed.
The main constraints of this abstraction are:
1. It relies on the time-stamps to determine which documents have been
indexed and which have not. This means that the time-stamps must be
monotonically increasing. The timestamp should be the timestamp
as measured by the server to minimize issues.
2. The record manager is currently implemented separately from the
vectorstore, which means that the overall system becomes distributed
and may create issues with consistency. For example, writing to
record manager succeeds but corresponding writing to vectorstore fails.
"""
def __init__(
self,