From 35b7d17d97cfeacf48a150dbf2267b5ec40dc8ba Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E9=BB=84=E8=85=BE?=
 <101850389+hangters@users.noreply.github.com>
Date: Wed, 11 Sep 2024 12:17:44 +0800
Subject: [PATCH] fix SILICONFLOW embedding error (#2363)

### What problem does this PR solve?

#2335  fix SILICONFLOW embedding error

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

---------

Co-authored-by: Zhedong Cen <cenzhedong2@126.com>
---
 rag/llm/embedding_model.py | 37 +++++++++++++++++++++++++++++++++----
 1 file changed, 33 insertions(+), 4 deletions(-)

diff --git a/rag/llm/embedding_model.py b/rag/llm/embedding_model.py
index 0c9ce73fc..7cfd3e319 100644
--- a/rag/llm/embedding_model.py
+++ b/rag/llm/embedding_model.py
@@ -577,11 +577,40 @@ class UpstageEmbed(OpenAIEmbed):
         super().__init__(key, model_name, base_url)
 
 
-class SILICONFLOWEmbed(OpenAIEmbed):
-    def __init__(self, key, model_name, base_url="https://api.siliconflow.cn/v1"):
+class SILICONFLOWEmbed(Base):
+    def __init__(
+        self, key, model_name, base_url="https://api.siliconflow.cn/v1/embeddings"
+    ):
         if not base_url:
-            base_url = "https://api.siliconflow.cn/v1"
-        super().__init__(key, model_name, base_url)
+            base_url = "https://api.siliconflow.cn/v1/embeddings"
+        self.headers = {
+            "accept": "application/json",
+            "content-type": "application/json",
+            "authorization": f"Bearer {key}",
+        }
+        self.base_url = base_url
+        self.model_name = model_name
+
+    def encode(self, texts: list, batch_size=32):
+        payload = {
+            "model": self.model_name,
+            "input": texts,
+            "encoding_format": "float",
+        }
+        res = requests.post(self.base_url, json=payload, headers=self.headers).json()
+        return (
+            np.array([d["embedding"] for d in res["data"]]),
+            res["usage"]["total_tokens"],
+        )
+
+    def encode_queries(self, text):
+        payload = {
+            "model": self.model_name,
+            "input": text,
+            "encoding_format": "float",
+        }
+        res = requests.post(self.base_url, json=payload, headers=self.headers).json()
+        return np.array(res["data"][0]["embedding"]), res["usage"]["total_tokens"]
 
 
 class ReplicateEmbed(Base):