Largely:
- Remove explicit `"Default is x"` since new refs show default inferred
from sig
- Inline code (useful for eventual parsing)
- Fix code block rendering (indentations)
**Description:** Fixes a small typo in `_get_document_with_hash` inside
`libs/core/langchain_core/indexing/api.py`.
**Issue:** N/A (no related issue)
**Dependencies:** None
**Description:** Fixes incorrect `num_skipped` count in the LangChain
indexing API. The current implementation only counts documents that
already exist in RecordManager (cross-batch duplicates) but fails to
count documents removed during within-batch deduplication via
`_deduplicate_in_order()`.
This PR adds tracking of the original batch size before deduplication
and includes the difference in `num_skipped`, ensuring that `num_added +
num_skipped` equals the total number of input documents.
**Issue:** Fixes incorrect document count reporting in indexing
statistics
**Dependencies:** None
Fixes#32272
---------
Co-authored-by: Alex Feel <afilippov@spotware.com>
Add additional hashing options to the indexing API, warn on SHA-1
Requires:
- Bumping langchain-core version
- bumping min langchain-core in langchain
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
`aindex` function should check not only `adelete` method, but `delete`
method too
**PR title**: "core: fix async indexing issue with adelete/delete
checking"
**PR message**: Currently `langchain.indexes.aindex` checks if vector
store has overrided adelete method. But due to `adelete` default
implementation store can have just `delete` overrided to make `adelete`
working.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
**Description:** Before this commit, if one record is batched in more
than 32k rows for sqlite3 >= 3.32 or more than 999 rows for sqlite3 <
3.31, the `record_manager.delete_keys()` will fail, as we are creating a
query with too many variables.
This commit ensures that we are batching the delete operation leveraging
the `cleanup_batch_size` as it is already done for `full` cleanup.
Added unit tests for incremental mode as well on different deleting
batch size.
Most easily reviewed with the "hide whitespace" option toggled.
Seeing 10-50% speed ups in import time for common structures 🚀
The general purpose of this PR is to lazily import structures within
`langchain_core.XXX_module.__init__.py` so that we're not eagerly
importing expensive dependencies (`pydantic`, `requests`, etc).
Analysis of flamegraphs generated with `importtime` motivated these
changes. For example, the one below demonstrates that importing
`HumanMessage` accidentally triggered imports for `importlib.metadata`,
`requests`, etc.
There's still much more to do on this front, and we can start digging
into our own internal code for optimizations now that we're less
concerned about external imports.
<img width="1210" alt="Screenshot 2025-04-11 at 1 10 54 PM"
src="https://github.com/user-attachments/assets/112a3fe7-24a9-4294-92c1-d5ae64df839e"
/>
I've tracked the improvements with some local benchmarks:
## `pytest-benchmark` results
| Name | Before (s) | After (s) | Delta (s) | % Change |
|-----------------------------|------------|-----------|-----------|----------|
| Document | 2.8683 | 1.2775 | -1.5908 | -55.46% |
| HumanMessage | 2.2358 | 1.1673 | -1.0685 | -47.79% |
| ChatPromptTemplate | 5.5235 | 2.9709 | -2.5526 | -46.22% |
| Runnable | 2.9423 | 1.7793 | -1.163 | -39.53% |
| InMemoryVectorStore | 3.1180 | 1.8417 | -1.2763 | -40.93% |
| RunnableLambda | 2.7385 | 1.8745 | -0.864 | -31.55% |
| tool | 5.1231 | 4.0771 | -1.046 | -20.42% |
| CallbackManager | 4.2263 | 3.4099 | -0.8164 | -19.32% |
| LangChainTracer | 3.8394 | 3.3101 | -0.5293 | -13.79% |
| BaseChatModel | 4.3317 | 3.8806 | -0.4511 | -10.41% |
| PydanticOutputParser | 3.2036 | 3.2995 | 0.0959 | 2.99% |
| InMemoryRateLimiter | 0.5311 | 0.5995 | 0.0684 | 12.88% |
Note the lack of change for `InMemoryRateLimiter` and
`PydanticOutputParser` is just random noise, I'm getting comparable
numbers locally.
## Local CodSpeed results
We're still working on configuring CodSpeed on CI. The local usage
produced similar results.
Looks like `pyupgrade` was already used here but missed some docs and
tests.
This helps to keep our docs looking professional and up to date.
Eventually, we should lint / format our inline docs.
The delete methods in the VectorStore and DocumentIndex interfaces
return a status indicating the result. Therefore, we can assume that
their implementations don't throw exceptions but instead return a result
indicating whether the delete operations have failed. The current
implementation doesn't check the returned value, so I modified it to
throw an exception when the operation fails.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
~Note that this PR is now Draft, so I didn't add change to `aindex`
function and didn't add test codes for my change.
After we have an agreement on the direction, I will add commits.~
`batch_size` is very difficult to decide because setting a large number
like >10000 will impact VectorDB and RecordManager, while setting a
small number will delete records unnecessarily, leading to redundant
work, as the `IMPORTANT` section says.
On the other hand, we can't use `full` because the loader returns just a
subset of the dataset in our use case.
I guess many people are in the same situation as us.
So, as one of the possible solutions for it, I would like to introduce a
new argument, `scoped_full_cleanup`.
This argument will be valid only when `claneup` is Full. If True, Full
cleanup deletes all documents that haven't been updated AND that are
associated with source ids that were seen during indexing. Default is
False.
This change keeps backward compatibility.
---------
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
I reported the bug 2 weeks ago here:
https://github.com/langchain-ai/langchain/issues/28447
I believe this is a critical bug for the indexer, so I submitted a PR to
revert the change and added unit tests to prevent similar bugs from
being introduced in the future.
@eyurtsev Could you check this?
Added `**kwargs` parameters to the `index` and `aindex` functions in
`libs/core/langchain_core/indexing/api.py`. This allows users to pass
additional arguments to the `add_documents` and `aadd_documents`
methods, enabling the specification of a custom `vector_field`. For
example, users can now use `vector_field="embedding"` when indexing
documents in `OpenSearchVectorStore`
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
