add support for Replicate (#1980)

### What problem does this PR solve?

#1853  add support for Replicate

### Type of change


- [x] New Feature (non-breaking change which adds functionality)

---------

Co-authored-by: Zhedong Cen <cenzhedong2@126.com>

rag/llm/__init__.py

@@ -42,7 +42,8 @@ EmbeddingModel = {
     "TogetherAI": TogetherAIEmbed,
     "PerfXCloud": PerfXCloudEmbed,
     "Upstage": UpstageEmbed,
-    "SILICONFLOW": SILICONFLOWEmbed
+    "SILICONFLOW": SILICONFLOWEmbed,
+    "Replicate": ReplicateEmbed
 }
 
 
@@ -96,7 +97,8 @@ ChatModel = {
     "Upstage":UpstageChat,
     "novita.ai": NovitaAIChat,
     "SILICONFLOW": SILICONFLOWChat,
-    "01.AI": YiChat
+    "01.AI": YiChat,
+    "Replicate": ReplicateChat
 }
 
 

rag/llm/chat_model.py

@@ -1003,7 +1003,7 @@ class TogetherAIChat(Base):
             base_url = "https://api.together.xyz/v1"
         super().__init__(key, model_name, base_url)
 
-      
+
 class PerfXCloudChat(Base):
     def __init__(self, key, model_name, base_url="https://cloud.perfxlab.cn/v1"):
         if not base_url:
@@ -1036,4 +1036,55 @@ class YiChat(Base):
     def __init__(self, key, model_name, base_url="https://api.01.ai/v1"):
         if not base_url:
             base_url = "https://api.01.ai/v1"
-        super().__init__(key, model_name, base_url)
+        super().__init__(key, model_name, base_url)
+
+
+class ReplicateChat(Base):
+    def __init__(self, key, model_name, base_url=None):
+        from replicate.client import Client
+
+        self.model_name = model_name
+        self.client = Client(api_token=key)
+        self.system = ""
+
+    def chat(self, system, history, gen_conf):
+        if "max_tokens" in gen_conf:
+            gen_conf["max_new_tokens"] = gen_conf.pop("max_tokens")
+        if system:
+            self.system = system
+        prompt = "\n".join(
+            [item["role"] + ":" + item["content"] for item in history[-5:]]
+        )
+        ans = ""
+        try:
+            response = self.client.run(
+                self.model_name,
+                input={"system_prompt": self.system, "prompt": prompt, **gen_conf},
+            )
+            ans = "".join(response)
+            return ans, num_tokens_from_string(ans)
+        except Exception as e:
+            return ans + "\n**ERROR**: " + str(e), 0
+
+    def chat_streamly(self, system, history, gen_conf):
+        if "max_tokens" in gen_conf:
+            gen_conf["max_new_tokens"] = gen_conf.pop("max_tokens")
+        if system:
+            self.system = system
+        prompt = "\n".join(
+            [item["role"] + ":" + item["content"] for item in history[-5:]]
+        )
+        ans = ""
+        try:
+            response = self.client.run(
+                self.model_name,
+                input={"system_prompt": self.system, "prompt": prompt, **gen_conf},
+            )
+            for resp in response:
+                ans += resp
+                yield ans
+
+        except Exception as e:
+            yield ans + "\n**ERROR**: " + str(e)
+
+        yield num_tokens_from_string(ans)

rag/llm/embedding_model.py

@@ -561,7 +561,7 @@ class TogetherAIEmbed(OllamaEmbed):
             base_url = "https://api.together.xyz/v1"
         super().__init__(key, model_name, base_url)
 
-      
+
 class PerfXCloudEmbed(OpenAIEmbed):
     def __init__(self, key, model_name, base_url="https://cloud.perfxlab.cn/v1"):
         if not base_url:
@@ -580,4 +580,22 @@ class SILICONFLOWEmbed(OpenAIEmbed):
     def __init__(self, key, model_name, base_url="https://api.siliconflow.cn/v1"):
         if not base_url:
             base_url = "https://api.siliconflow.cn/v1"
-        super().__init__(key, model_name, base_url)
+        super().__init__(key, model_name, base_url)
+
+
+class ReplicateEmbed(Base):
+    def __init__(self, key, model_name, base_url=None):
+        from replicate.client import Client
+
+        self.model_name = model_name
+        self.client = Client(api_token=key)
+
+    def encode(self, texts: list, batch_size=32):
+        from json import dumps
+
+        res = self.client.run(self.model_name, input={"texts": dumps(texts)})
+        return np.array(res), sum([num_tokens_from_string(text) for text in texts])
+
+    def encode_queries(self, text):
+        res = self.client.embed(self.model_name, input={"texts": [text]})
+        return np.array(res), num_tokens_from_string(text)