fix(langchain): correctly identify inputs for batch lcel chain calls (#…

…9195)

## Changes Made
Both `batch` and `invoke` calls on LCEL chains for LangChain pass
through the same traced method, for code re-use. However, the `kwarg`
for inputs to batch calls is `inputs`, while for invoke calls is
`input`. This change reflects that, and unit tests have been updated

## Motivation
Fix possible breaking customer apps (no reports yet as far as I can
tell).

## Checklist

- [x] Change(s) are motivated and described in the PR description
- [x] Testing strategy is described if automated tests are not included
in the PR
- [x] Risks are described (performance impact, potential for breakage,
maintainability)
- [x] Change is maintainable (easy to change, telemetry, documentation)
- [x] [Library release note
guidelines](https://ddtrace.readthedocs.io/en/stable/releasenotes.html)
are followed or label `changelog/no-changelog` is set
- [x] Documentation is included (in-code, generated user docs, [public
corp docs](https://github.com/DataDog/documentation/))
- [x] Backport labels are set (if
[applicable](https://ddtrace.readthedocs.io/en/latest/contributing.html#backporting))
- [x] If this PR changes the public interface, I've notified
`@DataDog/apm-tees`.

## Reviewer Checklist

- [ ] Title is accurate
- [ ] All changes are related to the pull request's stated goal
- [ ] Description motivates each change
- [ ] Avoids breaking
[API](https://ddtrace.readthedocs.io/en/stable/versioning.html#interfaces)
changes
- [ ] Testing strategy adequately addresses listed risks
- [ ] Change is maintainable (easy to change, telemetry, documentation)
- [ ] Release note makes sense to a user of the library
- [ ] Author has acknowledged and discussed the performance implications
of this PR as reported in the benchmarks PR comment
- [ ] Backport labels are set in a manner that is consistent with the
[release branch maintenance
policy](https://ddtrace.readthedocs.io/en/latest/contributing.html#backporting)

ddtrace/contrib/langchain/patch.py

@@ -731,7 +731,10 @@ def traced_lcel_runnable_sequence(langchain, pin, func, instance, args, kwargs):
     inputs = None
     final_output = None
     try:
-        inputs = get_argument_value(args, kwargs, 0, "input")
+        try:
+            inputs = get_argument_value(args, kwargs, 0, "input")
+        except ArgumentError:
+            inputs = get_argument_value(args, kwargs, 0, "inputs")
         if integration.is_pc_sampled_span(span):
             if not isinstance(inputs, list):
                 inputs = [inputs]
@@ -775,7 +778,10 @@ async def traced_lcel_runnable_sequence_async(langchain, pin, func, instance, ar
     inputs = None
     final_output = None
     try:
-        inputs = get_argument_value(args, kwargs, 0, "input")
+        try:
+            inputs = get_argument_value(args, kwargs, 0, "input")
+        except ArgumentError:
+            inputs = get_argument_value(args, kwargs, 0, "inputs")
         if integration.is_pc_sampled_span(span):
             if not isinstance(inputs, list):
                 inputs = [inputs]

releasenotes/notes/langchain-inputs-checking-f295eefb7053fd47.yaml

@@ -0,0 +1,4 @@
+---
+fixes:
+  - |
+    langchain: This fix resolves an issue where specifying inputs as a keyword argument for batching on chains caused a crash.

tests/contrib/langchain/test_langchain_community.py

@@ -1188,7 +1188,7 @@ def test_lcel_chain_batch(langchain_core, langchain_openai, request_vcr):
     chain = {"topic": langchain_core.runnables.RunnablePassthrough()} | prompt | model | output_parser
 
     with request_vcr.use_cassette("lcel_openai_chain_batch.yaml"):
-        chain.batch(["chickens", "pigs"])
+        chain.batch(inputs=["chickens", "pigs"])
 
 
 @flaky(1735812000)
@@ -1205,7 +1205,7 @@ def test_lcel_chain_batch_311(langchain_core, langchain_openai, request_vcr):
     chain = {"topic": langchain_core.runnables.RunnablePassthrough()} | prompt | model | output_parser
 
     with request_vcr.use_cassette("lcel_openai_chain_batch_311.yaml"):
-        chain.batch(["chickens", "pigs"])
+        chain.batch(inputs=["chickens", "pigs"])
 
 
 @flaky(1735812000)
@@ -1246,7 +1246,7 @@ async def test_lcel_chain_batch_async(langchain_core, langchain_openai, request_
     chain = {"topic": langchain_core.runnables.RunnablePassthrough()} | prompt | model | output_parser
 
     with request_vcr.use_cassette("lcel_openai_chain_batch_async.yaml"):
-        await chain.abatch(["chickens", "pigs"])
+        await chain.abatch(inputs=["chickens", "pigs"])
 
 
 @pytest.mark.parametrize(
@@ -1542,7 +1542,7 @@ def test_llmobs_chain_batch(
         chain = {"topic": langchain_core.runnables.RunnablePassthrough()} | prompt | model | output_parser
 
         self._test_llmobs_chain_invoke(
-            generate_trace=lambda inputs: chain.batch(["chickens", "pigs"]),
+            generate_trace=lambda inputs: chain.batch(inputs=["chickens", "pigs"]),
             request_vcr=request_vcr,
             mock_llmobs_span_writer=mock_llmobs_span_writer,
             mock_tracer=mock_tracer,