Langchain基础例子 - AI创想

from langchain_core.prompts import PromptTemplate
import os
from langchain_community.llms import Tongyi
DASHSCOPE_API_KEY ='用你自己的api_key'
os.environ["DASHSCOPE_API_KEY"]= DASHSCOPE_API_KEY
# llm = Tongyi()# 指定模型
llm = Tongyi(model_name="qwen-plus")# 默认qwen-plus, 为什么qwen-long不能用
template ="""Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate.from_template(template)
chain = prompt | llm
question ="What NFL team won the Super Bowl in the year Justin Bieber was born?"print(chain.invoke({"question": question}))

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'''
依赖dashscope
需要安装pip install dashscope
'''import os
from langchain_community.llms import Tongyi
os.environ["DASHSCOPE_API_KEY"]='用你自己的api_key'
text ="今天天气怎么样？"print(Tongyi().invoke(text))

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import ollama
back = ollama.chat(model="qwen2.5",messages=[{"role":"user","content":"简单介绍一下你自己"}],
options ={"temperature":1145141919810,# 模型温度"num_ctx":4096# 返回的最大token数},# ...
stream =False)# 是否流式输出)print(back.message['content'])

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# from langchain.chat_models import ChatOpenAIfrom langchain_community.chat_models import ChatOllama
from langchain_core.messages import HumanMessage
llm = ChatOllama(model="qwen2:7b")
messages =[
HumanMessage(
content="你好，请你介绍一下你自己",)]
response = llm.invoke(messages)print(response)

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# from langchain_community.llms import OpenAIfrom langchain_community.llms.ollama import Ollama
llm = Ollama(base_url='http://localhost:11434', model='qwen2:7b')print(llm('你是谁'))

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# from langchain_community.embeddings.openai import OpenAIEmbeddingsfrom langchain_community.embeddings import OllamaEmbeddings
ollama_emb = OllamaEmbeddings(base_url="http://127.0.0.1:11434", model="twwch/m3e-base:latest")
r1 = ollama_emb.embed_documents(["发动机点不着或","汽车停放一晚后亏电",])
r2 = ollama_emb.embed_query("我爱北京天安门")print(r1)print(r2)

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# -*- coding: utf-8 -*-import requests
import json
defsend_message_to_ollama(message, port=11434):
url =f"http://127.0.0.1:{port}/api/chat"
payload ={"model":"qwen2.5:7b","messages":[{"role":"user","content": message}]}
response = requests.post(url, json=payload)if response.status_code ==200:
response_content =""for line in response.iter_lines():if line:
response_content += json.loads(line)["message"]["content"]return response_content
else:returnf"Error: {response.status_code} - {response.text}"if __name__ =="__main__":
user_input ="介绍一下北京的旅游景点?"
response = send_message_to_ollama(user_input)print("Ollama's response:")print(response)

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from langchain.llms import Xinference
from langchain.prompts import PromptTemplate
llm = Xinference(server_url="http://localhost:9997", model_uid="qwen2:7b")
response = llm("你是谁")print(response)

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from langchain_community.embeddings import XinferenceEmbeddings
from langchain_community.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
# 1.文件加载
loader = TextLoader("三体.txt", encoding="UTF-8")
docs = loader.load()# 2.文本切割# text_splitter = CharacterTextSplitter(chunk_size=300, chunk_overlap=0)
text_splitter = RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=0)
texts = text_splitter.split_documents(docs)print(texts[0].page_content)# 3.向量化及存储
embeddings = XinferenceEmbeddings(server_url="http://127.0.0.1:9997", model_uid="bge-large-zh-v1.5")
embeded_texts = embeddings.embed_documents([t.page_content for t in texts])print(len(embeded_texts),len(embeded_texts[0]))
db = Chroma.from_documents(texts, embeddings)# 文本向量存储# 4.文本检索
query ="章北海是谁?"
query_vector = embeddings.embed_query(query)print(query_vector[:4])
docs = db.similarity_search_by_vector(query_vector, k=1)print(docs[0].page_content)

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# -*- coding: utf-8 -*-from langchain_community.document_loaders import DirectoryLoader,TextLoader
from langchain.text_splitter import CharacterTextSplitter,RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain.chains import ConversationalRetrievalChain, ChatVectorDBChain
from langchain.prompts import SystemMessagePromptTemplate,HumanMessagePromptTemplate,ChatPromptTemplate
from langchain_community.chat_models import ChatOllama
from langchain_community.embeddings import XinferenceEmbeddings
# 加载pdf文件# loader = DirectoryLoader(# './pdf/', glob="*.pdf", show_progress=True# )# 加载txt文件
loader = TextLoader('./txt/三体.txt')
docs = loader.load()# text_splitter = CharacterTextSplitter(chunk_size=1000,chunk_overlap=0)
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs_splitted = text_splitter.split_documents(docs)# embeddings = OpenAIEmbeddings()
embeddings = XinferenceEmbeddings(server_url="http://127.0.0.1:9997", model_uid="m3e-base")
db = Chroma.from_documents(
docs_splitted,
embeddings,
persist_directory="./chroma_db",)
retriever = db.as_retriever()
system_template ="""
使用下面的上下文来回答问题。如果你不知道答案，就说你不知道，不要编造答案。用中文回答这些问题。
-------------
{question}
-------------
{chat_history}
"""
messages =[
SystemMessagePromptTemplate.from_template(system_template),
HumanMessagePromptTemplate.from_template("{question}"),]
prompt = ChatPromptTemplate.from_messages(messages)# llm = ChatOpenAI(temperature=0.1, max_tokens=2048)
llm = ChatOllama(temperature=0.1, max_tokens=2048, model="qwen2.5")
qa = ConversationalRetrievalChain.from_llm(llm, retriever=retriever, condense_question_prompt=prompt)
chat_history =[]whileTrue:
question =input("请输入问题：")
result = qa({"question": question,"chat_history": chat_history})print(result["answer"])
chat_history.append((question, result["answer"]))

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# -*- coding: utf-8 -*-'''
llm和嵌入模型均调用Xinference接口
'''import streamlit as st
from langchain.llms import Xinference
from langchain.embeddings import XinferenceEmbeddings
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
# Customize the layout
st.set_page_config(page_title="Local AI Chat Powered by Xinference", page_icon=" ", layout="wide")# Write uploaded file in temp dirdefwrite_text_file(content, file_path):try:withopen(file_path,'w')asfile:file.write(content)returnTrueexcept Exception as e:print(f"Error occurred while writing the file: {e}")returnFalse# Prepare prompt template
prompt_template ="""
使用下面的上下文来回答问题。
如果你不知道答案，就说你不知道，不要编造答案。
{context}
问题: {question}
回答:"""
prompt = PromptTemplate(template=prompt_template, input_variables=["context","question"])# Initialize the Xinference LLM & Embeddings
xinference_server_url ="http://localhost:9997"
llm = Xinference(server_url=xinference_server_url, model_uid="my_llm")
embeddings = XinferenceEmbeddings(server_url=xinference_server_url, model_uid="my_embedding")
llm_chain = LLMChain(llm=llm, prompt=prompt)
st.title(" 文档对话")
uploaded_file = st.file_uploader("上传文件",type="txt")if uploaded_file isnotNone:
content = uploaded_file.read().decode('utf-8')
file_path ="/tmp/file.txt"
write_text_file(content, file_path)
loader = TextLoader(file_path)
docs = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=300, chunk_overlap=0)
texts = text_splitter.split_documents(docs)
db = Chroma.from_documents(texts, embeddings)
st.success("上传文档成功")# Query through LLM
question = st.text_input("提问", placeholder="请问我任何关于文章的问题", disabled=not uploaded_file)if question:
similar_doc = db.similarity_search(question, k=1)
st.write("相关上下文：")
st.write(similar_doc)
context = similar_doc[0].page_content
query_llm = LLMChain(llm=llm, prompt=prompt)
response = query_llm.run({"context": context,"question": question})
st.write(f"回答：{response}")

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# -*- coding: utf-8 -*-'''
基于Neo4j的电影情节向量检索器
1.Create the Neo4jVector from the moviePlots vector index.
2.Create the RetrievalQA chain using the Neo4jVector as the retriever.
3.Update the tools to use the RetrievalQA chain.
'''from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_community.graphs import Neo4jGraph
from langchain_community.vectorstores import Neo4jVector
OPENAI_API_KEY ="sk-..."
llm = ChatOpenAI(openai_api_key=OPENAI_API_KEY)
embedding_provider = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
graph = Neo4jGraph(
url="bolt://localhost:7687",
username="neo4j",
password="pleaseletmein")
movie_plot_vector = Neo4jVector.from_existing_index(
embedding_provider,
graph=graph,
index_name="moviePlots",
embedding_node_property="plotEmbedding",
text_node_property="plot",)
plot_retriever = RetrievalQA.from_llm(
llm=llm,
retriever=movie_plot_vector.as_retriever())
response = plot_retriever.invoke({"query":"A movie where a mission to the moon goes wrong"})print(response)

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# -*- coding: utf-8 -*-'''
电影预告片agent
'''from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_react_agent
from langchain.tools import Tool
from langchain import hub
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain.schema import StrOutputParser
from langchain_community.tools import YouTubeSearchTool
from langchain_community.chat_message_histories import Neo4jChatMessageHistory
from langchain_community.graphs import Neo4jGraph
from uuid import uuid4
SESSION_ID =str(uuid4())print(f"Session ID: {SESSION_ID}")
llm = ChatOpenAI(openai_api_key="sk-...")
graph = Neo4jGraph(
url="bolt://localhost:7687",
username="neo4j",
password="pleaseletmein")
prompt = ChatPromptTemplate.from_messages([("system","You are a movie expert. You find movies from a genre or plot.",),("human","{input}"),])
movie_chat = prompt | llm | StrOutputParser()
youtube = YouTubeSearchTool()defget_memory(session_id):return Neo4jChatMessageHistory(session_id=session_id, graph=graph)defcall_trailer_search(input):input=input.replace(","," ")return youtube.run(input)
tools =[
Tool.from_function(
name="Movie Chat",
description="For when you need to chat about movies. The question will be a string. Return a string.",
func=movie_chat.invoke,),
Tool.from_function(
name="Movie Trailer Search",
description="Use when needing to find a movie trailer. The question will include the word trailer. Return a link to a YouTube video.",
func=call_trailer_search,),]
agent_prompt = hub.pull("hwchase17/react-chat")
agent = create_react_agent(llm, tools, agent_prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools)
chat_agent = RunnableWithMessageHistory(
agent_executor,
get_memory,
input_messages_key="input",
history_messages_key="chat_history",)whileTrue:
q =input("> ")
response = chat_agent.invoke({"input": q
},{"configurable":{"session_id": SESSION_ID}},)print(response["output"])

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# -*- coding: utf-8 -*-from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_react_agent
from langchain.tools import Tool
from langchain import hub
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain.schema import StrOutputParser
from langchain_community.chat_message_histories import Neo4jChatMessageHistory
from langchain_community.graphs import Neo4jGraph
from uuid import uuid4
SESSION_ID =str(uuid4())print(f"Session ID: {SESSION_ID}")
llm = ChatOpenAI(openai_api_key="sk-...")
graph = Neo4jGraph(
url="bolt://localhost:7687",
username="neo4j",
password="pleaseletmein")
prompt = ChatPromptTemplate.from_messages([("system","You are a Neo4j expert having a conversation about how to create Cypher queries",),("human","{input}"),])
cypher_chat = prompt | llm | StrOutputParser()defget_memory(session_id):return Neo4jChatMessageHistory(session_id=session_id, graph=graph)
tools =[
Tool.from_function(
name="Cypher Support",
description="For when you need to talk about Cypher queries.",
func=cypher_chat.invoke,)]
agent_prompt = hub.pull("hwchase17/react-chat")
agent = create_react_agent(llm, tools, agent_prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools)
cypher_agent = RunnableWithMessageHistory(
agent_executor,
get_memory,
input_messages_key="input",
history_messages_key="chat_history",)whileTrue:
q =input("> ")
response = cypher_agent.invoke({"input": q
},{"configurable":{"session_id": SESSION_ID}},)print(response["output"])

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