fix(langchain): check chain.invoke() argument name [backport #8835 to…

… 2.8] (#8987)

Backport #8835 to 2.8.

This PR fixes the langchain integration's patched chain method to check
for the correct input argument name.

In `LangChain<0.1`, we patch `langchain.Chain.__call__()`, which uses
`inputs: Union[Dict[str, Any], str]` as the argument to the chain
invocation. However in `LangChain>=0.1`, we patch
`langchain.Chain.invoke()`, which uses `input: Dict[str, Any]` as the
argument to the chain invocation. We use the same traced function to
patch both methods, but this subtle change broke our argument parsing,
which expected the name `inputs` instead of `input`.

## 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`.
- [x] If change touches code that signs or publishes builds or packages,
or handles credentials of any kind, I've requested a review from
`@DataDog/security-design-and-guidance`.

## Reviewer Checklist

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

@@ -597,7 +597,10 @@ def traced_chain_call(langchain, pin, func, instance, args, kwargs):
     span = integration.trace(pin, "%s.%s" % (instance.__module__, instance.__class__.__name__), interface_type="chain")
     final_outputs = {}
     try:
-        inputs = get_argument_value(args, kwargs, 0, "inputs")
+        if SHOULD_PATCH_LANGCHAIN_COMMUNITY:
+            inputs = get_argument_value(args, kwargs, 0, "input")
+        else:
+            inputs = get_argument_value(args, kwargs, 0, "inputs")
         if not isinstance(inputs, dict):
             inputs = {instance.input_keys[0]: inputs}
         if integration.is_pc_sampled_span(span):
@@ -645,7 +648,10 @@ async def traced_chain_acall(langchain, pin, func, instance, args, kwargs):
     span = integration.trace(pin, "%s.%s" % (instance.__module__, instance.__class__.__name__), interface_type="chain")
     final_outputs = {}
     try:
-        inputs = get_argument_value(args, kwargs, 0, "inputs")
+        if SHOULD_PATCH_LANGCHAIN_COMMUNITY:
+            inputs = get_argument_value(args, kwargs, 0, "input")
+        else:
+            inputs = get_argument_value(args, kwargs, 0, "inputs")
         if not isinstance(inputs, dict):
             inputs = {instance.input_keys[0]: inputs}
         if integration.is_pc_sampled_span(span):

releasenotes/notes/fix-langchain-chain-invoke-792219fb95ac1889.yaml

@@ -0,0 +1,5 @@
+---
+fixes:
+  - |
+    langchain: This fix resolves an issue where tracing ``Chain.invoke()`` instead of ``Chain.__call__()`` resulted in
+    the an ``ArgumentError`` due to an argument name change for inputs between the two methods.

tests/contrib/langchain/test_langchain_community.py

@@ -484,6 +484,24 @@ def test_openai_math_chain_sync(langchain, langchain_openai, request_vcr):
         chain.invoke("what is two raised to the fifty-fourth power?")
 
 
+@pytest.mark.snapshot(token="tests.contrib.langchain.test_langchain_community.test_chain_invoke")
+def test_chain_invoke_dict_input(langchain, langchain_openai, request_vcr):
+    prompt_template = "what is {base} raised to the fifty-fourth power?"
+    prompt = langchain.prompts.PromptTemplate(input_variables=["adjective"], template=prompt_template)
+    chain = langchain.chains.LLMChain(llm=langchain_openai.OpenAI(temperature=0), prompt=prompt)
+    with request_vcr.use_cassette("openai_math_chain_sync.yaml"):
+        chain.invoke(input={"base": "two"})
+
+
+@pytest.mark.snapshot(token="tests.contrib.langchain.test_langchain_community.test_chain_invoke")
+def test_chain_invoke_str_input(langchain, langchain_openai, request_vcr):
+    prompt_template = "what is {base} raised to the fifty-fourth power?"
+    prompt = langchain.prompts.PromptTemplate(input_variables=["adjective"], template=prompt_template)
+    chain = langchain.chains.LLMChain(llm=langchain_openai.OpenAI(temperature=0), prompt=prompt)
+    with request_vcr.use_cassette("openai_math_chain_sync.yaml"):
+        chain.invoke("two")
+
+
 @pytest.mark.asyncio
 @pytest.mark.snapshot
 async def test_openai_math_chain_async(langchain, langchain_openai, request_vcr):

tests/snapshots/tests.contrib.langchain.test_langchain_community.test_chain_invoke.json

@@ -0,0 +1,71 @@
+[[
+  {
+    "name": "langchain.request",
+    "service": "",
+    "resource": "langchain.chains.llm.LLMChain",
+    "trace_id": 0,
+    "span_id": 1,
+    "parent_id": 0,
+    "type": "",
+    "error": 0,
+    "meta": {
+      "_dd.p.dm": "-0",
+      "_dd.p.tid": "660c678a00000000",
+      "langchain.request.inputs.base": "two",
+      "langchain.request.prompt": "what is {base} raised to the fifty-fourth power?",
+      "langchain.request.type": "chain",
+      "langchain.response.outputs.base": "two",
+      "langchain.response.outputs.text": "```text\\n2**54\\n```\\n...numexpr.evaluate(\"2**54\")...\\n",
+      "language": "python",
+      "runtime-id": "00697e1e790d47bb9828bc4ab549fac1"
+    },
+    "metrics": {
+      "_dd.measured": 1,
+      "_dd.top_level": 1,
+      "_dd.tracer_kr": 1.0,
+      "_sampling_priority_v1": 1,
+      "langchain.tokens.completion_tokens": 19,
+      "langchain.tokens.prompt_tokens": 202,
+      "langchain.tokens.total_cost": 0.00034100000000000005,
+      "langchain.tokens.total_tokens": 221,
+      "process_id": 61792
+    },
+    "duration": 31126000,
+    "start": 1712088970885366000
+  },
+     {
+       "name": "langchain.request",
+       "service": "",
+       "resource": "langchain_openai.llms.base.OpenAI",
+       "trace_id": 0,
+       "span_id": 2,
+       "parent_id": 1,
+       "type": "llm",
+       "error": 0,
+       "meta": {
+         "langchain.request.api_key": "...key>",
+         "langchain.request.model": "gpt-3.5-turbo-instruct",
+         "langchain.request.openai.parameters.frequency_penalty": "0",
+         "langchain.request.openai.parameters.max_tokens": "256",
+         "langchain.request.openai.parameters.model_name": "gpt-3.5-turbo-instruct",
+         "langchain.request.openai.parameters.n": "1",
+         "langchain.request.openai.parameters.presence_penalty": "0",
+         "langchain.request.openai.parameters.temperature": "0.0",
+         "langchain.request.openai.parameters.top_p": "1",
+         "langchain.request.prompts.0": "what is two raised to the fifty-fourth power?",
+         "langchain.request.provider": "openai",
+         "langchain.request.type": "llm",
+         "langchain.response.completions.0.finish_reason": "stop",
+         "langchain.response.completions.0.logprobs": "None",
+         "langchain.response.completions.0.text": "```text\\n2**54\\n```\\n...numexpr.evaluate(\"2**54\")...\\n"
+       },
+       "metrics": {
+         "_dd.measured": 1,
+         "langchain.tokens.completion_tokens": 19,
+         "langchain.tokens.prompt_tokens": 202,
+         "langchain.tokens.total_cost": 0.00034100000000000005,
+         "langchain.tokens.total_tokens": 221
+       },
+       "duration": 25786000,
+       "start": 1712088970890577000
+     }]]