add rag_chat.py

rag_chat.py

@@ -0,0 +1,42 @@
+from zhipuai import ZhipuAI
+ 
+client = ZhipuAI(api_key="c7e37f4df18095500882f7680e4496a9.63jqS9iptluarKXv") # 请填写您自己的APIKey
+ 
+result = client.knowledge.create(
+    embedding_id=3,
+    name="knowledge name",
+    description="knowledge description"
+)
+knowledge_id = result.id
+# print(result.id)
+
+
+resp = client.knowledge.document.create(
+    file=open("xxx.xlsx", "rb"),
+    purpose="retrieval",
+    knowledge_id=knowledge_id,
+    sentence_size=202,
+    custom_separator=["\n"]
+)
+print(resp)
+
+
+response = client.chat.completions.create(
+    model="glm-4",  # 填写需要调用的模型名称
+    messages=[
+        {"role": "user", "content": "你好！你叫什么名字"},
+    ],
+    tools=[
+            {
+                "type": "retrieval",
+                "retrieval": {
+                    "knowledge_id": "your knowledge id",
+                    "prompt_template": "从文档\n\"\"\"\n{{knowledge}}\n\"\"\"\n中找问题\n\"\"\"\n{{question}}\n\"\"\"\n的答案，找到答案就仅使用文档语句回答问题，找不到答案就用自身知识回答并且告诉用户该信息不是来自文档。\n不要复述问题，直接开始回答。"
+                }
+            }
+            ],
+    stream=True,
+)
+for chunk in response:
+    print(chunk.choices[0].delta)
+

requirements.txt

@@ -20,7 +20,7 @@ markdown-it-py==3.0.0
 MarkupSafe==2.1.5
 mdurl==0.1.2
 narwhals==1.5.5
-numpy==2.1.0
+numpy==2.0.2
 packaging==24.1
 pandas==2.2.2
 pillow==10.4.0