infernet-1.0.0 update

projects/tgi-llm/container/src/app.py

@@ -2,10 +2,11 @@ import logging
 import os
 from typing import Any, cast
 
-from eth_abi import decode, encode  # type: ignore
-from infernet_ml.utils.service_models import InfernetInput, InfernetInputSource
+from eth_abi.abi import decode, encode
+from infernet_ml.utils.service_models import InfernetInput, JobLocation
 from infernet_ml.workflows.inference.tgi_client_inference_workflow import (
     TGIClientInferenceWorkflow,
+    TgiInferenceRequest,
 )
 from quart import Quart, request
 
@@ -16,7 +17,7 @@ def create_app() -> Quart:
     app = Quart(__name__)
 
     workflow = TGIClientInferenceWorkflow(
-        server_url=cast(str, os.environ.get("TGI_SERVICE_URL"))
+        server_url=os.environ["TGI_SERVICE_URL"],
     )
 
     workflow.setup()
@@ -38,42 +39,51 @@ def create_app() -> Quart:
         """
         infernet_input: InfernetInput = InfernetInput(**req_data)
 
-        if infernet_input.source == InfernetInputSource.OFFCHAIN:
-            prompt = cast(dict[str, Any], infernet_input.data).get("prompt")
-        else:
-            # On-chain requests are sent as a generalized hex-string which we will
-            # decode to the appropriate format.
-            (prompt,) = decode(
-                ["string"], bytes.fromhex(cast(str, infernet_input.data))
-            )
+        match infernet_input:
+            case InfernetInput(source=JobLocation.OFFCHAIN):
+                prompt = cast(dict[str, Any], infernet_input.data).get("prompt")
+            case InfernetInput(source=JobLocation.ONCHAIN):
+                # On-chain requests are sent as a generalized hex-string which we will
+                # decode to the appropriate format.
+                (prompt,) = decode(
+                    ["string"], bytes.fromhex(cast(str, infernet_input.data))
+                )
+            case _:
+                raise ValueError("Invalid source")
 
-        result: dict[str, Any] = workflow.inference({"text": prompt})
+        result: dict[str, Any] = workflow.inference(
+            TgiInferenceRequest(text=cast(str, prompt))
+        )
 
-        if infernet_input.source == InfernetInputSource.OFFCHAIN:
-            """
-            In case of an off-chain request, the result is returned as a dict. The
-            infernet node expects a dict format.
-            """
-            return {"data": result}
-        else:
-            """
-            In case of an on-chain request, the result is returned in the format:
-            {
-                "raw_input": str,
-                "processed_input": str,
-                "raw_output": str,
-                "processed_output": str,
-                "proof": str,
-            }
-            refer to: https://docs.ritual.net/infernet/node/containers for more info.
-            """
-            return {
-                "raw_input": "",
-                "processed_input": "",
-                "raw_output": encode(["string"], [result]).hex(),
-                "processed_output": "",
-                "proof": "",
-            }
+        match infernet_input:
+            case InfernetInput(destination=JobLocation.OFFCHAIN):
+                """
+                In case of an off-chain request, the result is returned as a dict. The
+                infernet node expects a dict format.
+                """
+                return {"data": result}
+            case InfernetInput(destination=JobLocation.ONCHAIN):
+                """
+                In case of an on-chain request, the result is returned in the format:
+                {
+                    "raw_input": str,
+                    "processed_input": str,
+                    "raw_output": str,
+                    "processed_output": str,
+                    "proof": str,
+                }
+                refer to: https://docs.ritual.net/infernet/node/containers for more
+                info.
+                """
+                return {
+                    "raw_input": "",
+                    "processed_input": "",
+                    "raw_output": encode(["string"], [result]).hex(),
+                    "processed_output": "",
+                    "proof": "",
+                }
+            case _:
+                raise ValueError("Invalid destination")
 
     return app
 

projects/tgi-llm/container/src/requirements.txt

@@ -1,6 +1,5 @@
 quart==0.19.4
-infernet_ml==0.1.0
-PyArweave @ git+https://github.com/ritual-net/pyarweave.git
+infernet-ml==1.0.0
+infernet-ml[tgi_inference]==1.0.0
 web3==6.15.0
 retry2==0.9.5
-text-generation==0.6.1