184 lines
4.8 KiB
Python
184 lines
4.8 KiB
Python
import json
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import openai
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from langchain.chains import LLMChain
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from langchain.prompts import PromptTemplate
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import proto.documents_pb2
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from langchain import text_splitter
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from langchain.chains.qa_with_sources import load_qa_with_sources_chain
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from langchain.embeddings import OpenAIEmbeddings
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# from langchain.llms.openai import OpenAI;
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from langchain.chat_models.openai import ChatOpenAI
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from langchain.schema.document import Document
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from pymilvus import (
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connections,
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utility,
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FieldSchema,
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CollectionSchema,
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DataType,
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Collection,
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)
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import init
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import doc_client
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import openai
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from langchain.adapters import openai as lc_openai
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#
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# question = """
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# yarn : File C:\\Users\\ivamp\\AppData\\Roaming\\npm\\yarn.ps1 cannot be loaded because running scripts is disabled on this sy
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# stem. For more information, see about_Execution_Policies at https:/go.microsoft.com/fwlink/?LinkID=135170.
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# At line:1 char:1
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# + yarn config set registry https://registry.npm.taobao.org/
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# + ~~~~
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# + CategoryInfo : SecurityError: (:) [], PSSecurityException
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# + FullyQualifiedErrorId : UnauthorizedAccess
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#
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# 是什么问题，该怎么解决
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# """
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question = """
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错误 yarn 什么了遇到我
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"""
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#
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# vec = init.text_to_vector(question)
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vec = ""
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with open("../question_vec.json", "r") as f:
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vec = json.load(f)
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search_param = {
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"data": [vec],
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"anns_field": "vector",
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"param": {"metric_type": "L2"},
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"limit": 10,
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"expr": "user_id == 2",
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"output_fields": ["todo_id", "title", "source", "todo_description", "language", "text", "user_id"],
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}
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res = init.collection.search(**search_param)
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# 保留 5 个
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if len(res[0]) > 5:
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res[0] = res[0][:5]
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# document_chunk_ids = []
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# real_document = []
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plain_text = ""
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document_chunks = []
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for i in range(len(res[0])):
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_chunk_id = res[0][i].id
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print("正在获取分块 " + str(_chunk_id) + " 的内容...")
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try:
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_chunk_content = doc_client.stub.GetDocumentChunk(proto.documents_pb2.GetDocumentChunkByIdRequest(
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id=_chunk_id
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))
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# print(_chunk_content)
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# _doc_content_full = _chunk_content.content
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# print("DOC OBJ:" + _doc_content_full)
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document_chunks.append(_chunk_content.content)
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plain_text += "=== \n" + _chunk_content.content + " ===\n"
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# real_document.append(_doc_content)
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# doc_obj = Document(page_content=_doc_content_full, metadata={"source": _chunk_content.title})
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# doc_obj = Document(page_content=_doc_content_full, metadata={"source": "chunked content"})
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# real_document.append(doc_obj)
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except Exception as e:
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print(e)
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# print(real_document)
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print("正在调用 LLM...")
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# prompt_template = f"""Answer questions use the following context and reply in question spoken language and answer
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# with Markdown format, you can fix syntax errors in the context, but do not change the meaning of the context.
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# you can tell user context errors(syntax or meaning) in answer.
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# ---
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# {plain_text}
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# ---
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# Question: {question}
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# Answer:"""
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messages = [
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{
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"role": "system",
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"content": """
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回答问题使用以下上下文，并以提问的语言和Markdown回答，并告诉来源。
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你得用“你”的身份指代用户。如果用户的问题有语法错误或者上下文的意思不对，你可以告诉用户。
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请辨别上下文中的内容，有一些是不相干的。
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"""
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},
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{
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"role": "system",
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"content": f"""
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{plain_text}
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"""
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},
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{
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"role": "user",
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"content": f"""
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{question}
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"""
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}
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]
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result = openai.ChatCompletion.create(
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messages=messages, model="gpt-3.5-turbo", temperature=0
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)
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res = result["choices"][0]["message"].to_dict_recursive()
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print(res)
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# prompt_template = f"""
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# ---
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# {plain_text}
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# ---
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# Question: {question}
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# Answer:"""
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#
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# print(prompt_template)
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# # PROMPT = PromptTemplate(
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# # template=prompt_template, input_variables=["real_document", "question"]
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# # )
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#
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#
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# ChatOpenAI
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# llm = OpenAI(temperature=0, model_name="gpt-3.5-turbo")
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# # chain = LLMChain(llm=llm, prompt=PROMPT)
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#
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# output = llm(prompt_template)
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# gpt = openai.Completion.create(
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# engine="gpt-3.5-turbo",
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# prompt=prompt_template,
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# max_tokens=150,
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# temperature=0,
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# top_p=1,
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# frequency_penalty=0,
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# presence_penalty=0,
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# stop=["==="]
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# )
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# output = gpt["choices"][0]["text"]
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# print(output)
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# output = chain({"real_document": real_document, "question": question}, return_only_outputs=True)
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# print(output)
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# chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="map_reduce", return_intermediate_steps=True,
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# verbose=True)
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#
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# question = "必须使用中文回复:" + question
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# output = chain({"input_documents": real_document, "question": question}, return_only_outputs=False)
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# print("回复：" + output["output_text"])
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