Fix init to accept kwargs

agent.json

@@ -1,4 +1,31 @@
 {
+  "reranker.predict": {
+    "traces": [],
+    "train": [],
+    "demos": [],
+    "signature": {
+      "instructions": "Assess the relevance of a document to a query.",
+      "fields": [
+        {
+          "prefix": "Query:",
+          "description": "${query}"
+        },
+        {
+          "prefix": "Document:",
+          "description": "${document}"
+        },
+        {
+          "prefix": "Reasoning: Let's think step by step in order to",
+          "description": "${reasoning}"
+        },
+        {
+          "prefix": "Relevance Score:",
+          "description": "${relevance_score}"
+        }
+      ]
+    },
+    "lm": null
+  },
   "metadata": {
     "dependency_versions": {
       "python": "3.11",

auto_classes.json

@@ -1,4 +1,4 @@
 {
-  "AutoConfig": "hello.EchoConfig",
-  "AutoAgent": "hello.EchoAgent"
+  "AutoConfig": "ce_ranker.CERankerConfig",
+  "AutoAgent": "ce_ranker.CERankerAgent"
 }

ce_ranker.py

@@ -0,0 +1,78 @@
+from dotenv import load_dotenv
+import os
+import asyncio
+
+import dspy
+from modaic import PrecompiledAgent, PrecompiledConfig
+import weaviate
+
+load_dotenv()
+
+
+class RelevanceAssessment(dspy.Signature):
+    """Assess the relevance of a document to a query."""
+    query: str = dspy.InputField()
+    document: str = dspy.InputField()
+    relevance_score: bool = dspy.OutputField()
+
+
+class CERankerConfig(PrecompiledConfig):
+    lm: str = "openai/gpt-4.1-mini"
+    collection_name: str
+    return_properties: list[str]
+
+
+class CERankerAgent(PrecompiledAgent):
+    config: CERankerConfig
+
+    def __init__(self, config: CERankerConfig, **kwargs):
+        super().__init__(config, **kwargs)
+        
+        lm = dspy.LM(self.config.lm)
+        dspy.configure(lm=lm)
+        
+        self._connect_to_weaviate()
+        self.reranker = dspy.ChainOfThought(RelevanceAssessment)
+
+    def _connect_to_weaviate(self):
+        self.weaviate_client = weaviate.connect_to_weaviate_cloud(
+            cluster_url=os.getenv("WEAVIATE_URL"),
+            auth_credentials=weaviate.auth.AuthApiKey(os.getenv("WEAVIATE_API_KEY")),
+        )
+        self.collection = self.weaviate_client.collections.get(
+            self.config.collection_name
+        )
+
+    async def _score_document(self, query: str, document: str) -> tuple[str, bool]:
+        result = await self.reranker.acall(query=query, document=document)
+        return (document, result.relevance_score)
+
+    async def __acall__(self, query: str, k: int = 1) -> str:
+        response = self.collection.query.hybrid(query=query, limit=k)
+        
+        documents = [o.properties["content"] for o in response.objects]
+        
+        scored_results = await asyncio.gather(
+            *[self._score_document(query, doc) for doc in documents]
+        )
+        
+        scored_results.sort(key=lambda x: x[1], reverse=True)
+        
+        return "\n".join([doc for doc, score in scored_results[:k]])
+
+    def __call__(self, query: str, k: int = 1) -> str:
+        return asyncio.run(self.__acall__(query, k))
+
+
+if __name__ == "__main__":
+    config = CERankerConfig(
+        collection_name="IRPapersText_Default",
+        return_properties=["content"]
+    )
+    agent = CERankerAgent(config)
+    print(agent(query="What is HyDE?"))
+    agent.push_to_hub(
+        "connor/CrossEncoderRanker",
+        with_code=True,
+        commit_message="Fix init to accept kwargs"
+    )

config.json

@@ -1,4 +1,5 @@
 {
+  "lm": "openai/gpt-4.1-mini",
   "collection_name": "IRPapersText_Default",
   "return_properties": [
     "content"

hello.py

@@ -1,49 +0,0 @@
-from dotenv import load_dotenv
-import os
-
-from modaic import PrecompiledAgent, PrecompiledConfig
-import weaviate
-
-load_dotenv()
-
-class EchoConfig(PrecompiledConfig):
-    collection_name: str
-    return_properties: list[str]
-
-class EchoAgent(PrecompiledAgent):
-    config: EchoConfig
-
-    def __init__(self, config: EchoConfig, **kwargs):
-        super().__init__(config, **kwargs)
-        self.weaviate_client = weaviate.connect_to_weaviate_cloud(
-            cluster_url=os.getenv("WEAVIATE_URL"),
-            auth_credentials=weaviate.auth.AuthApiKey(os.getenv("WEAVIATE_API_KEY")),
-        )
-        self.collection = self.weaviate_client.collections.get(
-            self.config.collection_name
-        )
-
-    def forward(self, query: str) -> str:
-        response = self.collection.query.hybrid(
-            query=query,
-            limit=1
-        )
-        results = []
-        for o in response.objects:
-            results.append(o.properties["content"])
-
-        return "\n".join(results)
-
-
-if __name__ == "__main__":
-    config = EchoConfig(
-        collection_name="IRPapersText_Default",
-        return_properties=["content"]
-    )
-    agent = EchoAgent(config)
-    print(agent(query="What is HyDE?"))
-    agent.push_to_hub(
-        "connor/CrossEncoderRanker",
-        with_code=True,
-        commit_message="Fix init to accept kwargs"
-    )