Upload 3 files

app.py

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+import asyncio
+import json
+import logging
+from copy import deepcopy
+from dataclasses import asdict
+from typing import Dict, List, Union
+
+import janus
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from lagent.schema import AgentStatusCode
+from pydantic import BaseModel
+from sse_starlette.sse import EventSourceResponse
+
+from mindsearch.agent import init_agent
+
+
+def parse_arguments():
+    import argparse
+    parser = argparse.ArgumentParser(description='MindSearch API')
+    parser.add_argument('--lang', default='cn', type=str, help='Language')
+    parser.add_argument('--model_format',
+                        default='internlm_server',
+                        type=str,
+                        help='Model format')
+    parser.add_argument('--search_engine',
+                       default='DuckDuckGoSearch',
+                       type=str,
+                       help='Search engine')
+    return parser.parse_args()
+
+
+args = parse_arguments()
+app = FastAPI(docs_url='/')
+
+app.add_middleware(CORSMiddleware,
+                   allow_origins=['*'],
+                   allow_credentials=True,
+                   allow_methods=['*'],
+                   allow_headers=['*'])
+
+
+class GenerationParams(BaseModel):
+    inputs: Union[str, List[Dict]]
+    agent_cfg: Dict = dict()
+
+
+@app.post('/solve')
+async def run(request: GenerationParams):
+
+    def convert_adjacency_to_tree(adjacency_input, root_name):
+
+        def build_tree(node_name):
+            node = {'name': node_name, 'children': []}
+            if node_name in adjacency_input:
+                for child in adjacency_input[node_name]:
+                    child_node = build_tree(child['name'])
+                    child_node['state'] = child['state']
+                    child_node['id'] = child['id']
+                    node['children'].append(child_node)
+            return node
+
+        return build_tree(root_name)
+
+    async def generate():
+        try:
+            queue = janus.Queue()
+            stop_event = asyncio.Event()
+
+            # Wrapping a sync generator as an async generator using run_in_executor
+            def sync_generator_wrapper():
+                try:
+                    for response in agent.stream_chat(inputs):
+                        queue.sync_q.put(response)
+                except Exception as e:
+                    logging.exception(
+                        f'Exception in sync_generator_wrapper: {e}')
+                finally:
+                    # Notify async_generator_wrapper that the data generation is complete.
+                    queue.sync_q.put(None)
+
+            async def async_generator_wrapper():
+                loop = asyncio.get_event_loop()
+                loop.run_in_executor(None, sync_generator_wrapper)
+                while True:
+                    response = await queue.async_q.get()
+                    if response is None:  # Ensure that all elements are consumed
+                        break
+                    yield response
+                    if not isinstance(
+                            response,
+                            tuple) and response.state == AgentStatusCode.END:
+                        break
+                stop_event.set()  # Inform sync_generator_wrapper to stop
+
+            async for response in async_generator_wrapper():
+                if isinstance(response, tuple):
+                    agent_return, node_name = response
+                else:
+                    agent_return = response
+                    node_name = None
+                origin_adj = deepcopy(agent_return.adjacency_list)
+                adjacency_list = convert_adjacency_to_tree(
+                    agent_return.adjacency_list, 'root')
+                assert adjacency_list[
+                    'name'] == 'root' and 'children' in adjacency_list
+                agent_return.adjacency_list = adjacency_list['children']
+                agent_return = asdict(agent_return)
+                agent_return['adj'] = origin_adj
+                response_json = json.dumps(dict(response=agent_return,
+                                                current_node=node_name),
+                                           ensure_ascii=False)
+                yield {'data': response_json}
+                # yield f'data: {response_json}\n\n'
+        except Exception as exc:
+            msg = 'An error occurred while generating the response.'
+            logging.exception(msg)
+            response_json = json.dumps(
+                dict(error=dict(msg=msg, details=str(exc))),
+                ensure_ascii=False)
+            yield {'data': response_json}
+            # yield f'data: {response_json}\n\n'
+        finally:
+            await stop_event.wait(
+            )  # Waiting for async_generator_wrapper to stop
+            queue.close()
+            await queue.wait_closed()
+
+    inputs = request.inputs
+    agent = init_agent(lang=args.lang, model_format=args.model_format,search_engine=args.search_engine)
+    return EventSourceResponse(generate())
+
+
+if __name__ == '__main__':
+    import uvicorn
+    uvicorn.run(app, host='0.0.0.0', port=8002, log_level='info')

requirements.txt

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+duckduckgo_search==5.3.1b1
+einops
+fastapi
+git+https://github.com/InternLM/lagent.git
+gradio
+janus
+lmdeploy
+pyvis
+sse-starlette
+termcolor
+transformers==4.41.0
+uvicorn

terminal.py

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+from datetime import datetime
+
+from lagent.actions import ActionExecutor, BingBrowser
+from lagent.llms import INTERNLM2_META, LMDeployServer
+
+from mindsearch.agent.mindsearch_agent import (MindSearchAgent,
+                                               MindSearchProtocol)
+from mindsearch.agent.mindsearch_prompt import (
+    FINAL_RESPONSE_CN, FINAL_RESPONSE_EN, GRAPH_PROMPT_CN, GRAPH_PROMPT_EN,
+    searcher_context_template_cn, searcher_context_template_en,
+    searcher_input_template_cn, searcher_input_template_en,
+    searcher_system_prompt_cn, searcher_system_prompt_en)
+
+lang = 'cn'
+llm = LMDeployServer(path='internlm/internlm2_5-7b-chat',
+                     model_name='internlm2',
+                     meta_template=INTERNLM2_META,
+                     top_p=0.8,
+                     top_k=1,
+                     temperature=0,
+                     max_new_tokens=8192,
+                     repetition_penalty=1.02,
+                     stop_words=['<|im_end|>'])
+
+agent = MindSearchAgent(
+    llm=llm,
+    protocol=MindSearchProtocol(
+        meta_prompt=datetime.now().strftime('The current date is %Y-%m-%d.'),
+        interpreter_prompt=GRAPH_PROMPT_CN
+        if lang == 'cn' else GRAPH_PROMPT_EN,
+        response_prompt=FINAL_RESPONSE_CN
+        if lang == 'cn' else FINAL_RESPONSE_EN),
+    searcher_cfg=dict(
+        llm=llm,
+        plugin_executor=ActionExecutor(
+            BingBrowser(searcher_type='DuckDuckGoSearch', topk=6)),
+        protocol=MindSearchProtocol(
+            meta_prompt=datetime.now().strftime(
+                'The current date is %Y-%m-%d.'),
+            plugin_prompt=searcher_system_prompt_cn
+            if lang == 'cn' else searcher_system_prompt_en,
+        ),
+        template=dict(input=searcher_input_template_cn
+                      if lang == 'cn' else searcher_input_template_en,
+                      context=searcher_context_template_cn
+                      if lang == 'cn' else searcher_context_template_en)),
+    max_turn=10)
+
+for agent_return in agent.stream_chat('上海今天适合穿什么衣服'):
+    pass