feat(rerank): add Text Embeddings Inference (TEI) API support for reranking (#2516)

Co-authored-by: tam <tanwe@fulan.com.cn>

packages/dbgpt-core/src/dbgpt/rag/embedding/rerank.py

@@ -392,6 +392,107 @@ class SiliconFlowRerankEmbeddings(OpenAPIRerankEmbeddings):
         return scores
 
 
+@dataclass
+class TeiEmbeddingsParameters(OpenAPIRerankerDeployModelParameters):
+    """Text Embeddings Inference  Rerank Embeddings Parameters."""
+
+    provider: str = "proxy/tei"
+
+    api_url: str = field(
+        default="http://localhost:8001/rerank",
+        metadata={
+            "help": _("The URL of the rerank API."),
+        },
+    )
+    api_key: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": _("The API key for the rerank API."),
+        },
+    )
+
+
+class TeiRerankEmbeddings(OpenAPIRerankEmbeddings):
+    """Text Embeddings Inference Rerank Model.
+
+    See `Text Embeddings Inference API
+    <https://huggingface.github.io/text-embeddings-inference/>`_ for more details.
+    """
+
+    def __init__(self, **kwargs: Any):
+        """Initialize the TeiRerankEmbeddings."""
+        # If the API key is not provided, try to get it from the environment
+        if "api_key" not in kwargs:
+            kwargs["api_key"] = os.getenv("TEI_API_KEY")
+
+        if "api_url" not in kwargs:
+            raise ValueError("Please provide the api_url param")
+
+        super().__init__(**kwargs)
+
+    @classmethod
+    def param_class(cls) -> Type[TeiEmbeddingsParameters]:
+        """Get the parameter class."""
+        return TeiEmbeddingsParameters
+
+    def _parse_results(self, response: List[Dict[str, Any]]) -> List[float]:
+        """Parse the response from the API.
+
+        Args:
+            response: The response from the API.
+
+        Returns:
+            List[float]: The rank scores of the candidates.
+        """
+        if not isinstance(response, list) and len(response) == 0:
+            raise RuntimeError("Results should be a not empty list")
+
+        # Sort by index, 0 in the first element
+        results = sorted(response, key=lambda x: x.get("index", 0))
+        scores = [float(result.get("score")) for result in results]
+        return scores
+
+    def predict(self, query: str, candidates: List[str]) -> List[float]:
+        """Predict the rank scores of the candidates.
+
+        Args:
+            query: The query text.
+            candidates: The list of candidate texts.
+
+        Returns:
+            List[float]: The rank scores of the candidates.
+        """
+        if not candidates:
+            return []
+        headers = {}
+        current_span_id = root_tracer.get_current_span_id()
+        if self.pass_trace_id and current_span_id:
+            # Set the trace ID if available
+            headers[DBGPT_TRACER_SPAN_ID] = current_span_id
+        data = {"query": query, "texts": candidates}
+        response = self.session.post(  # type: ignore
+            self.api_url, json=data, timeout=self.timeout, headers=headers
+        )
+        response.raise_for_status()
+        return self._parse_results(response.json())
+
+    async def apredict(self, query: str, candidates: List[str]) -> List[float]:
+        """Predict the rank scores of the candidates asynchronously."""
+        headers = {"Authorization": f"Bearer {self.api_key}"}
+        current_span_id = root_tracer.get_current_span_id()
+        if self.pass_trace_id and current_span_id:
+            # Set the trace ID if available
+            headers[DBGPT_TRACER_SPAN_ID] = current_span_id
+        async with aiohttp.ClientSession(
+            headers=headers, timeout=aiohttp.ClientTimeout(total=self.timeout)
+        ) as session:
+            data = {"query": query, "texts": candidates}
+            async with session.post(self.api_url, json=data) as resp:
+                resp.raise_for_status()
+                response_data = await resp.json()
+                return self._parse_results(response_data)
+
+
 register_embedding_adapter(
     CrossEncoderRerankEmbeddings, supported_models=RERANKER_COMMON_HF_MODELS
 )
@@ -401,3 +502,6 @@ register_embedding_adapter(
 register_embedding_adapter(
     SiliconFlowRerankEmbeddings, supported_models=RERANKER_COMMON_HF_MODELS
 )
+register_embedding_adapter(
+    TeiRerankEmbeddings, supported_models=RERANKER_COMMON_HF_MODELS
+)