mirror of
https://github.com/primedigitaltech/FileChat.git
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Merge branch 'main' of https://github.com/czl-helloworld/FileChat
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czl
commit
d6b38a9c16
@ -46,3 +46,9 @@
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```
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streamlit run file_chat.py
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```
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## 一些相关开发技术文档链接
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* Streamlit： https://docs.streamlit.io/
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* 智谱文件问答API：https://bigmodel.cn/dev/howuse/fileqa
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* 智谱RAG问答API：https://bigmodel.cn/dev/howuse/retrieval
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@ -24,6 +24,7 @@ def main():
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"请在侧边栏输入API_KEY。可从"
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" https://bigmodel.cn/usercenter/apikeys 获取"
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)
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st.stop()
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client = ZhipuAI(api_key=api_key)
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uploaded_file = st.file_uploader(
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"上传文件",
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@ -38,6 +39,7 @@ def main():
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if "messages" not in st.session_state:
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file_object = client.files.create(file=uploaded_file, purpose="file-extract")
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file_content = json.loads(client.files.content(file_id=file_object.id).content)["content"]
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client.files.delete(file_id=file_object.id)
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message_content = f"请对\n{file_content}\n的内容进行分析，并撰写一份论文摘要。"
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response = client.chat.completions.create(
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model="glm-4-long",
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166
rag_chat.py
166
rag_chat.py
@ -1,42 +1,160 @@
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import os
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import time
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import pandas as pd
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import streamlit as st
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from zhipuai import ZhipuAI
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from zhipuai.types.knowledge import KnowledgeInfo
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client = ZhipuAI(api_key="") # 请填写您自己的APIKey
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result = client.knowledge.create(
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embedding_id=3,
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name="knowledge name",
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description="knowledge description"
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def init_client() -> ZhipuAI:
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api_key = st.session_state.get("API_KEY")
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if not api_key:
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st.warning(
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"请在侧边栏输入API_KEY。可从"
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" https://bigmodel.cn/usercenter/apikeys 获取"
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)
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knowledge_id = result.id
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# print(result.id)
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st.stop()
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return ZhipuAI(api_key=api_key)
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resp = client.knowledge.document.create(
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file=open("xxx.xlsx", "rb"),
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purpose="retrieval",
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knowledge_id=knowledge_id,
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sentence_size=202,
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custom_separator=["\n"]
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def config_sidebar() -> None:
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with st.sidebar:
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st.markdown("调用智谱AI知识库对话接口进行问答")
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st.text_input(
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"智谱API Key",
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type="password",
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value=os.environ.get("API_KEY", None)
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or st.session_state.get("API_KEY", ""),
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key="API_KEY",
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)
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st.button(
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"清空对话",
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use_container_width=True,
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on_click=lambda: st.session_state.pop("messages", None)
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)
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print(resp)
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def config_kb(client: ZhipuAI) -> KnowledgeInfo:
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kb_usage = client.knowledge.used()
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existing_kb = client.knowledge.query(page=1, size=10)
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st.code(f"现有{existing_kb.total}个知识库，已使用{kb_usage.used.word_num}字，共{kb_usage.total.word_num}字") # 使用量
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selected_kb_index = st.session_state.get("selected_kb_index", 0)
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with st.expander("知识库信息", expanded=True):
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selected_kb = st.selectbox(
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"请选择或新建知识库",
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existing_kb.list+["新建知识库"],
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format_func=lambda x: x.name if not isinstance(x, str) else x,
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index=selected_kb_index
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)
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if selected_kb == "新建知识库":
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create_kb(client)
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st.stop()
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uploaded_files = st.file_uploader(
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f"上传文件至知识库{selected_kb.name}",
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accept_multiple_files=True,
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type=["pdf", "doc", "docx", "xlsx"],
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help="文件大小不超过50M",
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)
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kb_files = client.knowledge.document.list(purpose="retrieval", knowledge_id=selected_kb.id)
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st.write("知识库文件列表")
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st.write(pd.DataFrame([file.to_dict() for file in kb_files.list]), unsafe_allow_html=True)
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cols = st.columns(2)
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with cols[0]:
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upload_btn = st.button("上传文件", use_container_width=True)
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with cols[1]:
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delete_kb = st.button("删除知识库", use_container_width=True)
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if delete_kb:
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drop_kb(client, selected_kb)
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if upload_btn:
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if not uploaded_files:
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st.warning("请先上传文件")
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st.stop()
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else:
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for uploaded_file in uploaded_files:
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client.files.create(file=uploaded_file, purpose="retrieval", knowledge_id=selected_kb.id)
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st.success(f"上传文件成功")
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st.rerun()
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return selected_kb
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def chat_with_kb(client: ZhipuAI, kb: KnowledgeInfo) -> None:
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st.session_state["messages"] = st.session_state.get("messages", [])
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for msg in st.session_state.get("messages", []):
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st.chat_message(msg["role"]).write(msg["content"])
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if prompt := st.chat_input():
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msg = {"role": "user", "content": prompt}
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st.session_state.messages.append(msg)
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st.chat_message("user").write(prompt)
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response = client.chat.completions.create(
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model="glm-4", # 填写需要调用的模型名称
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messages=[
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{"role": "user", "content": "你好！你叫什么名字"},
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],
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tools=[
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{
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messages=[msg],
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tools=[{
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"type": "retrieval",
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"retrieval": {
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"knowledge_id": "your knowledge id",
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"knowledge_id": kb.id,
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"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。"
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}
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}
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],
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}],
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stream=True,
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)
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for chunk in response:
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print(chunk.choices[0].delta)
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reply = st.chat_message("assistant").write_stream(chunk.choices[0].delta.content for chunk in response)
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st.session_state.messages.append({"role": "assistant", "content": reply})
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def create_kb(client: ZhipuAI) -> None:
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with st.form("新建知识库"):
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kb_name = st.text_input(
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"知识库名称",
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help="知识库名称，限制为20字以内。",
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placeholder="知识库名称，限制为20字以内。",
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key="kb_name",
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)
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kb_description = st.text_input(
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"知识库描述",
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help="知识库描述，限制为100字以内。",
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placeholder="知识库描述，限制为100字以内。",
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key="kb_info",
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)
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kb_embedding_id = st.selectbox(
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"向量化模型",
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options=["3"],
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help="知识库绑定的向量化模型，目前仅支持embedding-2。\n3:表示为embedding-2",
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key="kb_embedding",
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)
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submit = st.form_submit_button("新建")
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if submit:
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result = client.knowledge.create(
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embedding_id=kb_embedding_id,
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name=kb_name,
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description=kb_description,
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)
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st.success(f"新建知识库成功，知识库ID为{result.id}")
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time.sleep(0.5)
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st.rerun()
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def drop_kb(client: ZhipuAI, kb: KnowledgeInfo) -> None:
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client.knowledge.delete(kb.id)
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st.success(f"删除知识库{kb.name}成功")
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time.sleep(0.5)
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st.rerun()
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def main():
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st.set_page_config(page_title="知识库对话demo", page_icon="📖", layout="wide") # 页面设置
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st.header("📖知识库对话demo") # 标题
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config_sidebar()
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client = init_client()
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selected_kb = config_kb(client)
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chat_with_kb(client, selected_kb)
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if __name__ == "__main__":
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main()
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