Description: Similar in concept to the `MarkdownHeaderTextSplitter`, the
`HTMLHeaderTextSplitter` is a "structure-aware" chunker that splits text
at the element level and adds metadata for each header "relevant" to any
given chunk. It can return chunks element by element or combine elements
with the same metadata, with the objectives of (a) keeping related text
grouped (more or less) semantically and (b) preserving context-rich
information encoded in document structures. It can be used with other
text splitters as part of a chunking pipeline.
Dependency: lxml python package
Maintainer: @hwchase17
Twitter handle: @MartinZirulnik
---------
Co-authored-by: PresidioVantage <github@presidiovantage.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
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- **Description:** Doc corrections and resolve notebook rendering issue
on GH
- **Issue:** N/A
- **Dependencies:** N/A
- **Tag maintainer:** @baskaryan
- **Twitter handle:** `@isaacchung1217`
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
**Description:**
Examples in the "Select by similarity" section were not really
highlighting capabilities of similarity search.
E.g. "# Input is a measurement, so should select the tall/short example"
was still outputting the "mood" example.
I tweaked the inputs a bit and fixed the examples (checking that those
are indeed what the search outputs).
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
I've refactored the code to ensure that ImportError is consistently
handled. Instead of using ValueError as before, I've now followed the
standard practice of raising ImportError along with clear and
informative error messages. This change enhances the code's clarity and
explicitly signifies that any problems are associated with module
imports.
- **Description:** Fix typo about `RetrievalQAWithSourceChain` ->
`RetrievalQAWithSourcesChain`
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- **Description:** Adds Kotlin language to `TextSplitter`
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
For external libraries that depend on `type_to_cls_dict`, adds a
workaround to continue using the old format.
Recommend people use `get_type_to_cls_dict()` instead and only resolve
the imports when they're used.
- **Description:** use term keyword according to the official python doc
glossary, see https://docs.python.org/3/glossary.html
- **Issue:** not applicable
- **Dependencies:** not applicable
- **Tag maintainer:** @hwchase17
- **Twitter handle:** vreyespue
The previous API of the `_execute()` function had a few rough edges that
this PR addresses:
- The `fetch` argument was type-hinted as being able to take any string,
but any string other than `"all"` or `"one"` would `raise ValueError`.
The new type hints explicitly declare that only those values are
supported.
- The return type was type-hinted as `Sequence` but using `fetch =
"one"` would actually return a single result item. This was incorrectly
suppressed using `# type: ignore`. We now always return a list.
- Using `fetch = "one"` would return a single item if data was found, or
an empty *list* if no data was found. This was confusing, and we now
always return a list to simplify.
- The return type was `Sequence[Any]` which was a bit difficult to use
since it wasn't clear what one could do with the returned rows. I'm
making the new type `Dict[str, Any]` that corresponds to the column
names and their values in the query.
I've updated the use of this method elsewhere in the file to match the
new behavior.
continuation of PR #8550
@hwchase17 please see and merge. And also close the PR #8550.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
therefor -> therefore
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Instead of:
```
client = Client()
with collect_runs() as cb:
chain.invoke()
run = cb.traced_runs[0]
client.get_run_url(run)
```
it's
```
with tracing_v2_enabled() as cb:
chain.invoke()
cb.get_run_url()
```
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---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Similarly to Vertex classes, PaLM classes weren't marked as
serialisable. Should be working fine with LangSmith.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
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This PR uses 2 dedicated LangChain warnings types for deprecations
(mirroring python's built in deprecation and pending deprecation
warnings).
These deprecation types are unslienced during initialization in
langchain achieving the same default behavior that we have with our
current warnings approach. However, because these warnings have a
dedicated type, users will be able to silence them selectively (I think
this is strictly better than our current handling of warnings).
The PR adds a deprecation warning to llm symbolic math.
---------
Co-authored-by: Predrag Gruevski <2348618+obi1kenobi@users.noreply.github.com>
- Also move RunnableBranch to its own file
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MattiaSangermano