LangChain完整指南 Python中的AI驱动语言建模

from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.llms import OpenAI
import os

os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"
os.environ["SERPAPI_API_KEY"] = "YOUR_SERP_API_KEY"  # 在这里获取您的Serp API密钥:https://serpapi.com/

OpenAI.api_key = "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"
llm = OpenAI(model="gpt-3.5-turbo", temperature=0)
tools = load_tools(["serpapi", "llm-math"], llm=llm)
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run("风力涡轮机在2022年全球产生了多少能量?")

from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.agents.agent_types import AgentType
from langchain.agents import create_csv_agent
import os

os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

agent = create_csv_agent(
    OpenAI(temperature=0),
    "netflix_titles.csv",
    verbose=True,
    agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
)

agent.run("Christian Bale出演了多少部电影?")

from langchain.agents import create_pandas_dataframe_agent
from langchain.chat_models import ChatOpenAI
from langchain.agents.agent_types import AgentType
from langchain.llms import OpenAI
import pandas as pd
import os

os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_KEY"
df = pd.read_csv("netflix_titles.csv")

agent = create_pandas_dataframe_agent(OpenAI(temperature=0), df, verbose=True)

agent.run("什么年份发布了最多的喜剧电影?")

from langchain.llms import OpenAI
import os
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

llm = OpenAI(model="gpt-3.5-turbo", temperature=0.9)
print(llm("为Matt Nikonorov想一个说唱艺名"))

from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.schema import (
    AIMessage,
    HumanMessage,
    SystemMessage
)
import os
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

chat = ChatOpenAI()
messages = [
    SystemMessage(content="你是一个友好、非正式的助手"),
    HumanMessage(content="说服我Djokovic比Federer更好")
]
print(chat(messages))

from langchain.embeddings import OpenAIEmbeddings

embeddings_model = OpenAIEmbeddings()
embedded_query = embeddings_model.embed_query("谁创造了万维网?")
embedded_query[:5]

from langchain.embeddings import OpenAIEmbeddings
from openai.embeddings_utils import cosine_similarity
import os
import pandas as pd

os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_KEY"
embeddings_model = OpenAIEmbeddings()

df = pd.read_csv("embs.csv")

# 对每个信息生成嵌入
emb1 = embeddings_model.embed_query(df["text"][0])
emb2 = embeddings_model.embed_query(df["text"][1])
emb3 = embeddings_model.embed_query(df["text"][2])
emb_list = [emb1, emb2, emb3]
df["embedding"] = emb_list

embedded_question = embeddings_model.embed_query("有史以来最高的人是谁?") # 生成问题的嵌入
df["similarity"] = df.embedding.apply(lambda x: cosine_similarity(x, embedded_question)) # 找到每个数据片段与问题的相关性
df.to_csv("embs.csv")
df2 = df.sort_values("similarity", ascending=False) # 根据与问题的关联性对信息片段进行排序
print(df2["text"][0])

from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(separators=["\n\n", "\n"], chunk_size=2000, chunk_overlap=250)
texts = text_splitter.split_text(your_text)

from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=2000,
    chunk_overlap=250,
    length_function=len,
    add_start_index=True,
)
texts = text_splitter.create_documents([your_text])

from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
import os
os.environ["OPENAI_API_KEY"] = "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"

llm = OpenAI(temperature=0.9)
prompt = PromptTemplate(
    input_variables=["media", "topic"],
    template="为{media}关于{topic}制作一个好标题",
)
chain = LLMChain(llm=llm, prompt=prompt)
print(chain.run({
    'media': "恐怖电影",
    'topic': "数学"
}))

from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from typing import Optional

from langchain.chains.openai_functions import (
    create_openai_fn_chain,
    create_structured_output_chain,
)
import os

os.environ["OPENAI_API_KEY"] = "YOUR_KEY"

llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.1)
template = """按照给定的格式从以下输入中提取信息:{input}。确保以正确的格式回答"""
prompt = PromptTemplate(template=template, input_variables=["input"])

json_schema = {
    "type": "object",
    "properties": {
        "name": {"title": "Name", "description": "The artist's name", "type": "string"},
        "genre": {"title": "Genre", "description": "The artist's music genre", "type": "string"},
        "debut": {"title": "Debut", "description": "The artist's debut album", "type": "string"},
        "debut_year": {"title": "Debut_year", "description":
        ```python
        "Year of artist's debut album", "type": "integer"}
    },
    "required": ["name", "genre", "debut", "debut_year"],
}

chain = create_structured_output_chain(json_schema, llm, prompt, verbose=False)
f = open("Nas.txt", "r")
artist_info = str(f.read())
print(chain.run(artist_info))

from langchain.llms import Cohere
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain

template = """问题:{question}

答案:让我们一步一步地思考。"""
prompt = PromptTemplate(template=template, input_variables=["question"])
llm = Cohere(cohere_api_key="YOUR_COHERE_KEY")
llm_chain = LLMChain(prompt=prompt, llm=llm)

question = "Novak Djokovic是在哪一天出生的?"

print(llm_chain.run(question))