upload system message

src/bisheng-langchain/experimental/document_ie_v2/config.json

@@ -1,9 +1,9 @@
 {
   "unstructured_api_url": "https://bisheng.dataelem.com/api/v1/etl4llm/predict",
-  "llm_model_name": "qwen1.5",
-  "llm_model_api_url": "http://34.87.129.78:9300/v1",
-  "server_type": "openai_api",
+  "llm_model_name": "qwen1.5-72b-chat",
+  "llm_model_api_url": "https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation",
+  "server_type": "qwen_api",
   "web_host": "192.168.106.20",
-  "web_port": "7118",
+  "web_port": 8118,
   "api_port": 6118
 }

src/bisheng-langchain/experimental/document_ie_v2/document_extract.py

@@ -5,14 +5,12 @@
 import os
 import tempfile
 
-from flask import Flask, Response, abort, request
+from flask import Flask, Response, request
 from llm_extract import LlmExtract, init_logger
 from prompt import system_template
 from tqdm import tqdm
 
 logger = init_logger(__name__)
-
-
 app = Flask(__name__)
 
 
@@ -44,7 +42,7 @@ def predict_one_pdf(self, pdf_path, schema, system_message=system_template, save
                 with open(save_llm_path, 'r') as f:
                     llm_kv_results = json.load(f)
         else:
-            llm_kv_results = self.llm_client.predict(pdf_path, schema)
+            llm_kv_results = self.llm_client.predict(pdf_path, schema, system_message)
 
         return llm_kv_results
 

src/bisheng-langchain/experimental/document_ie_v2/run_web.py

@@ -16,8 +16,7 @@
 unstructured_api_url = config['unstructured_api_url']
 llm_model_name = config['llm_model_name']
 llm_model_api_url = config['llm_model_api_url']
-
-server_type = 'openai_api'
+server_type = config['server_type']
 web_host = config['web_host']
 web_port = config['web_port']