初始化项目，由ModelHub XC社区提供模型

Model: AgentPublic/guillaumetell-7b
Source: Original Platform

prompt_demo_analysis.py

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+
+#A demo of a side functionality of Guillaume-Tell: guessing whether the question should open up a source retrieval pipeline.
+#The function should return a structured answer in json with two components:
+##A short analysis with reasoning.
+##A boolean answer in French ("oui" or "non")
+
+
+import sys, os
+from pprint import pprint
+from jinja2 import Environment, FileSystemLoader, meta
+import yaml
+
+import pandas as pd
+from vllm import LLM, SamplingParams
+
+
+sys.path.append(".")
+os.chdir(os.path.dirname(os.path.abspath(__file__)))
+
+#Specific function to deal with json format.
+def get_llm_response(prompt_template, sampling_params):
+  prompts = [prompt_template]
+  outputs = llm.generate(prompts, sampling_params, use_tqdm = False)
+  generated_text = outputs[0].outputs[0].text
+  if generated_text[-1] != "}":
+    generated_text = generated_text + "}"
+  prompt = prompt_template + generated_text
+  return prompt, generated_text
+
+if __name__ == "__main__":
+
+    with open('prompt_config.yaml') as f:
+        config = yaml.safe_load(f)
+
+    print("prompt format:", config.get("prompt_format"))
+    print(config)
+    print()
+    for prompt in config["prompts"]:
+        if prompt["mode"] == "analysis":
+            print(f'--- prompt mode: {prompt["mode"]} ---')
+            env = Environment(loader=FileSystemLoader("."))
+            template = env.get_template(prompt["template"])
+
+            source = template.environment.loader.get_source(template.environment, template.name)
+            variables = meta.find_undeclared_variables(env.parse(source[0]))
+
+            print("variables:", variables)
+            print("---")
+
+            data = {"query": "Comment obtenir le formulaire A36 ?"}
+            if "system_prompt" in variables:
+                data["system_prompt"] = prompt["system_prompt"]
+
+            rendered_template = template.render(**data)
+            print(rendered_template)
+            print("---")
+
+            llm = LLM("mistral-mfs-reference-2/mistral-mfs-reference-2")
+
+            sampling_params = SamplingParams(temperature=0.2, top_p=0.95, max_tokens=300, stop="}")
+            
+            prompt, generated_text = get_llm_response(rendered_template, sampling_params)
+            print("Albert : ", generated_text)