add support for LocalLLM (#1744)

### What problem does this PR solve?

add support for LocalLLM

### Type of change

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

---------

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

rag/llm/chat_model.py

 import os 
 import json
 import requests
+import asyncio
+from rag.svr.jina_server import Prompt,Generation
 
 class Base(ABC):
     def __init__(self, key, model_name, base_url):
 
         def __conn(self):
             from multiprocessing.connection import Client
+
             self._connection = Client(
-                (self.host, self.port), authkey=b'infiniflow-token4kevinhu')
+                (self.host, self.port), authkey=b"infiniflow-token4kevinhu"
+            )
 
         def __getattr__(self, name):
             import pickle
             def do_rpc(*args, **kwargs):
                 for _ in range(3):
                     try:
-                        self._connection.send(
-                            pickle.dumps((name, args, kwargs)))
+                        self._connection.send(pickle.dumps((name, args, kwargs)))
                         return pickle.loads(self._connection.recv())
                     except Exception as e:
                         self.__conn()
 
             return do_rpc
 
-    def __init__(self, key, model_name="glm-3-turbo"):
-        self.client = LocalLLM.RPCProxy("127.0.0.1", 7860)
+    def __init__(self, key, model_name):
+        from jina import Client
 
-    def chat(self, system, history, gen_conf):
-        if system:
-            history.insert(0, {"role": "system", "content": system})
-        try:
-            ans = self.client.chat(
-                history,
-                gen_conf
-            )
-            return ans, num_tokens_from_string(ans)
-        except Exception as e:
-            return "**ERROR**: " + str(e), 0
+        self.client = Client(port=12345, protocol="grpc", asyncio=True)
 
-    def chat_streamly(self, system, history, gen_conf):
+    def _prepare_prompt(self, system, history, gen_conf):
         if system:
             history.insert(0, {"role": "system", "content": system})
-        token_count = 0
+        if "max_tokens" in gen_conf:
+            gen_conf["max_new_tokens"] = gen_conf.pop("max_tokens")
+        return Prompt(message=history, gen_conf=gen_conf)
+
+    def _stream_response(self, endpoint, prompt):
         answer = ""
         try:
-            for ans in self.client.chat_streamly(history, gen_conf):
-                answer += ans
-                token_count += 1
-                yield answer
+            res = self.client.stream_doc(
+                on=endpoint, inputs=prompt, return_type=Generation
+            )
+            loop = asyncio.get_event_loop()
+            try:
+                while True:
+                    answer = loop.run_until_complete(res.__anext__()).text
+                    yield answer
+            except StopAsyncIteration:
+                pass
         except Exception as e:
             yield answer + "\n**ERROR**: " + str(e)
+        yield num_tokens_from_string(answer)
+
+    def chat(self, system, history, gen_conf):
+        prompt = self._prepare_prompt(system, history, gen_conf)
+        chat_gen = self._stream_response("/chat", prompt)
+        ans = next(chat_gen)
+        total_tokens = next(chat_gen)
+        return ans, total_tokens
 
-        yield token_count
+    def chat_streamly(self, system, history, gen_conf):
+        prompt = self._prepare_prompt(system, history, gen_conf)
+        return self._stream_response("/stream", prompt)
 
 
 class VolcEngineChat(Base):

rag/svr/jina_server.py

+from jina import Deployment
+from docarray import BaseDoc
+from jina import Executor, requests
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+import argparse
+import torch
+
+
+class Prompt(BaseDoc):
+    message: list[dict]
+    gen_conf: dict
+
+
+class Generation(BaseDoc):
+    text: str
+
+
+tokenizer = None
+model_name = ""
+
+
+class TokenStreamingExecutor(Executor):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_name, device_map="auto", torch_dtype="auto"
+        )
+
+    @requests(on="/chat")
+    async def generate(self, doc: Prompt, **kwargs) -> Generation:
+        text = tokenizer.apply_chat_template(
+            doc.message,
+            tokenize=False,
+        )
+        inputs = tokenizer([text], return_tensors="pt")
+        generation_config = GenerationConfig(
+            **doc.gen_conf,
+            eos_token_id=tokenizer.eos_token_id,
+            pad_token_id=tokenizer.eos_token_id
+        )
+        generated_ids = self.model.generate(
+            inputs.input_ids, generation_config=generation_config
+        )
+        generated_ids = [
+            output_ids[len(input_ids) :]
+            for input_ids, output_ids in zip(inputs.input_ids, generated_ids)
+        ]
+
+        response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+        yield Generation(text=response)
+
+    @requests(on="/stream")
+    async def task(self, doc: Prompt, **kwargs) -> Generation:
+        text = tokenizer.apply_chat_template(
+            doc.message,
+            tokenize=False,
+        )
+        input = tokenizer([text], return_tensors="pt")
+        input_len = input["input_ids"].shape[1]
+        max_new_tokens = 512
+        if "max_new_tokens" in doc.gen_conf:
+            max_new_tokens = doc.gen_conf.pop("max_new_tokens")
+        generation_config = GenerationConfig(
+            **doc.gen_conf,
+            eos_token_id=tokenizer.eos_token_id,
+            pad_token_id=tokenizer.eos_token_id
+        )
+        for _ in range(max_new_tokens):
+            output = self.model.generate(
+                **input, max_new_tokens=1, generation_config=generation_config
+            )
+            if output[0][-1] == tokenizer.eos_token_id:
+                break
+            yield Generation(
+                text=tokenizer.decode(output[0][input_len:], skip_special_tokens=True)
+            )
+            input = {
+                "input_ids": output,
+                "attention_mask": torch.ones(1, len(output[0])),
+            }
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model_name", type=str, help="Model name or path")
+    parser.add_argument("--port", default=12345, type=int, help="Jina serving port")
+    args = parser.parse_args()
+    model_name = args.model_name
+    tokenizer = AutoTokenizer.from_pretrained(args.model_name)
+    with Deployment(
+        uses=TokenStreamingExecutor, port=args.port, protocol="grpc"
+    ) as dep:
+        dep.block()