feat: support rerank models provided by vLLM, Xinference, Bedrock

backend/app/rag/chat_config.py

@@ -21,6 +21,8 @@
 from llama_index.embeddings.ollama import OllamaEmbedding
 from llama_index.postprocessor.jinaai_rerank import JinaRerank
 from llama_index.postprocessor.cohere_rerank import CohereRerank
+from llama_index.postprocessor.xinference_rerank import XinferenceRerank
+from llama_index.postprocessor.bedrock_rerank import AWSBedrockRerank
 from sqlmodel import Session
 from google.oauth2 import service_account
 from google.auth.transport.requests import Request
@@ -30,6 +32,7 @@
 from app.rag.node_postprocessor.metadata_post_filter import MetadataFilters
 from app.rag.node_postprocessor.baisheng_reranker import BaishengRerank
 from app.rag.node_postprocessor.local_reranker import LocalRerank
+from app.rag.node_postprocessor.vllm_reranker import VLLMRerank
 from app.rag.embeddings.local_embedding import LocalEmbedding
 from app.repositories import chat_engine_repo, knowledge_base_repo
 from app.repositories.embedding_model import embed_model_repo
@@ -423,6 +426,27 @@ def get_reranker_model(
                 top_n=top_n,
                 **config,
             )
+        case RerankerProvider.VLLM:
+            return VLLMRerank(
+                model=model,
+                top_n=top_n,
+                **config,
+            )
+        case RerankerProvider.XINFERENCE:
+            return XinferenceRerank(
+                model=model,
+                top_n=top_n,
+                **config,
+            )
+        case RerankerProvider.BEDROCK:
+            return AWSBedrockRerank(
+                rerank_model_name=model,
+                top_n=top_n,
+                aws_access_key_id=credentials["aws_access_key_id"],
+                aws_secret_access_key=credentials["aws_secret_access_key"],
+                region_name=credentials["aws_region_name"],
+                **config,
+            )
         case _:
             raise ValueError(f"Got unknown reranker provider: {provider}")
 

backend/app/rag/node_postprocessor/vllm_reranker.py

@@ -0,0 +1,97 @@
+from typing import Any, List, Optional
+import requests
+
+from llama_index.core.bridge.pydantic import Field, PrivateAttr
+from llama_index.core.callbacks import CBEventType, EventPayload
+from llama_index.core.instrumentation import get_dispatcher
+from llama_index.core.instrumentation.events.rerank import (
+    ReRankEndEvent,
+    ReRankStartEvent,
+)
+from llama_index.core.postprocessor.types import BaseNodePostprocessor
+from llama_index.core.schema import MetadataMode, NodeWithScore, QueryBundle
+
+dispatcher = get_dispatcher(__name__)
+
+
+class VLLMRerank(BaseNodePostprocessor):
+    base_url: str = Field(default="", description="The base URL of vLLM API.")
+    model: str = Field(default="", description="The model to use when calling API.")
+
+    top_n: int = Field(description="Top N nodes to return.")
+
+    _session: Any = PrivateAttr()
+
+    def __init__(
+        self,
+        top_n: int = 2,
+        model: str = "BAAI/bge-reranker-v2-m3",
+        base_url: str = "http://localhost:8000",
+    ):
+        super().__init__(top_n=top_n, model=model)
+        self.base_url = base_url
+        self.model = model
+        self._session = requests.Session()
+
+    @classmethod
+    def class_name(cls) -> str:
+        return "VLLMRerank"
+
+    def _postprocess_nodes(
+        self,
+        nodes: List[NodeWithScore],
+        query_bundle: Optional[QueryBundle] = None,
+    ) -> List[NodeWithScore]:
+        dispatcher.event(
+            ReRankStartEvent(
+                query=query_bundle,
+                nodes=nodes,
+                top_n=self.top_n,
+                model_name=self.model,
+            )
+        )
+
+        if query_bundle is None:
+            raise ValueError("Missing query bundle in extra info.")
+        if len(nodes) == 0:
+            return []
+
+        with self.callback_manager.event(
+            CBEventType.RERANKING,
+            payload={
+                EventPayload.NODES: nodes,
+                EventPayload.MODEL_NAME: self.model,
+                EventPayload.QUERY_STR: query_bundle.query_str,
+                EventPayload.TOP_K: self.top_n,
+            },
+        ) as event:
+            texts = [
+                node.node.get_content(metadata_mode=MetadataMode.EMBED)
+                for node in nodes
+            ]
+            resp = self._session.post(  # type: ignore
+                url=f"{self.base_url}/v1/score",
+                json={
+                    "text_1": query_bundle.query_str,
+                    "model": self.model,
+                    "text_2": texts,
+                },
+            )
+            resp.raise_for_status()
+            resp_json = resp.json()
+            if "data" not in resp_json:
+                raise RuntimeError(f"Got error from reranker: {resp_json}")
+
+            results = zip(range(len(nodes)), resp_json["data"])
+            results = sorted(results, key=lambda x: x[1]["score"], reverse=True)[: self.top_n]
+
+            new_nodes = []
+            for result in results:
+                new_node_with_score = NodeWithScore(
+                    node=nodes[result[0]].node, score=result[1]["score"]
+                )
+                new_nodes.append(new_node_with_score)
+            event.on_end(payload={EventPayload.NODES: new_nodes})
+
+        dispatcher.event(ReRankEndEvent(nodes=new_nodes))
+        return new_nodes

backend/app/rag/reranker_model_option.py

@@ -77,4 +77,53 @@ class RerankerModelOption(BaseModel):
         credentials_type="str",
         default_credentials="dummy",
     ),
+    RerankerModelOption(
+        provider=RerankerProvider.VLLM,
+        provider_display_name="vLLM",
+        provider_description="vLLM is a fast and easy-to-use library for LLM inference and serving.",
+        default_reranker_model="BAAI/bge-reranker-v2-m3",
+        reranker_model_description="Reference: https://docs.vllm.ai/en/latest/models/supported_models.html#sentence-pair-scoring-task-score",
+        default_top_n=10,
+        default_config={
+            "base_url": "http://localhost:8000",
+        },
+        config_description="base_url is the base url of the vLLM server, ensure it can be accessed from this server",
+        credentials_display_name="vLLM API Key",
+        credentials_description="vLLM doesn't require an API key, set a dummy string here is ok",
+        credentials_type="str",
+        default_credentials="dummy",
+    ),
+    RerankerModelOption(
+        provider=RerankerProvider.XINFERENCE,
+        provider_display_name="Xinference Reranker",
+        provider_description="Xorbits Inference (Xinference) is an open-source platform to streamline the operation and integration of a wide array of AI models.",
+        default_reranker_model="bge-reranker-v2-m3",
+        reranker_model_description="Reference: https://inference.readthedocs.io/en/latest/models/model_abilities/rerank.html",
+        default_top_n=10,
+        default_config={
+            "base_url": "http://localhost:9997",
+        },
+        config_description="base_url is the url of the Xinference server, ensure it can be accessed from this server",
+        credentials_display_name="Xinference API Key",
+        credentials_description="Xinference doesn't require an API key, set a dummy string here is ok",
+        credentials_type="str",
+        default_credentials="dummy",
+    ),
+    RerankerModelOption(
+        provider=RerankerProvider.BEDROCK,
+        provider_display_name="Bedrock Reranker",
+        provider_description="Amazon Bedrock is a fully managed foundation models service.",
+        provider_url="https://docs.aws.amazon.com/bedrock/",
+        default_reranker_model="amazon.rerank-v1:0",
+        reranker_model_description="Find more models in https://docs.aws.amazon.com/bedrock/latest/userguide/foundation-models-reference.html.",
+        default_top_n=10,
+        credentials_display_name="AWS Bedrock Credentials JSON",
+        credentials_description="The JSON Object of AWS Credentials, refer to https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-files.html#cli-configure-files-global",
+        credentials_type="dict",
+        default_credentials={
+            "aws_access_key_id": "****",
+            "aws_secret_access_key": "****",
+            "aws_region_name": "us-west-2",
+        },
+    )
 ]

backend/app/types.py

@@ -27,6 +27,9 @@ class RerankerProvider(str, enum.Enum):
     COHERE = "cohere"
     BAISHENG = "baisheng"
     LOCAL = "local"
+    VLLM = "vllm"
+    XINFERENCE = "xinference"
+    BEDROCK = "bedrock"
 
 
 class MimeTypes(str, enum.Enum):

backend/pyproject.toml

@@ -46,10 +46,10 @@ dependencies = [
     "llama-index-postprocessor-cohere-rerank>=0.1.7",
     "llama-index-llms-bedrock>=0.1.12",
     "pypdf>=4.3.1",
-    "llama-index-llms-ollama<=0.3.0",
-    "llama-index-embeddings-ollama<=0.3.0",
-    "llama-index-embeddings-jinaai<=0.3.0",
-    "llama-index-embeddings-cohere<=0.3.0",
+    "llama-index-llms-ollama>=0.3.0",
+    "llama-index-embeddings-ollama>=0.3.0",
+    "llama-index-embeddings-jinaai>=0.3.0",
+    "llama-index-embeddings-cohere>=0.2.0",
     "python-docx>=1.1.2",
     "python-pptx>=1.0.2",
     "colorama>=0.4.6",
@@ -58,7 +58,9 @@ dependencies = [
     "retry>=0.9.2",
     "langchain-openai>=0.2.9",
     "ragas>=0.2.6",
-    "llama-index-embeddings-bedrock<=0.3.0",
+    "llama-index-embeddings-bedrock>=0.2.0",
+    "llama-index-postprocessor-xinference-rerank>=0.2.0",
+    "llama-index-postprocessor-bedrock-rerank>=0.3.0",
 ]
 readme = "README.md"
 requires-python = ">= 3.8"