mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
a91181fe6d
Added support for optionally supplying 'Guardrails for Amazon Bedrock'
on both types of model invocations (batch/regular and streaming) and for
all models supported by the Amazon Bedrock service.
@baskaryan @hwchase17
```python
llm = Bedrock(model_id="<model_id>", client=bedrock,
model_kwargs={},
guardrails={"id": " <guardrail_id>",
"version": "<guardrail_version>",
"trace": True}, callbacks=[BedrockAsyncCallbackHandler()])
class BedrockAsyncCallbackHandler(AsyncCallbackHandler):
"""Async callback handler that can be used to handle callbacks from langchain."""
async def on_llm_error(
self,
error: BaseException,
**kwargs: Any,
) -> Any:
reason = kwargs.get("reason")
if reason == "GUARDRAIL_INTERVENED":
# kwargs contains additional trace information sent by 'Guardrails for Bedrock' service.
print(f"""Guardrails: {kwargs}""")
# streaming
llm = Bedrock(model_id="<model_id>", client=bedrock,
model_kwargs={},
streaming=True,
guardrails={"id": "<guardrail_id>",
"version": "<guardrail_version>"})
```
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
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|---|---|---|
| .. | ||
| adapters | ||
| callbacks | ||
| chat_message_histories | ||
| chat_models | ||
| document_loaders | ||
| embeddings | ||
| examples | ||
| graphs | ||
| llms | ||
| retrievers | ||
| storage | ||
| tools | ||
| utilities | ||
| vectorstores | ||
| __init__.py | ||
| .env.example | ||
| conftest.py | ||
| test_compile.py | ||