【LangChain系列2】【Model I/O详解】

pip install langchain

chain = prompt | chatmodel | outputparser
chain.invoke({"input":"What's your name?")

from langchain.prompts.few_shot import FewShotPromptTemplate
from langchain.prompts.prompt import PromptTemplate

examples = [
  {
    "question": "Who lived longer, Muhammad Ali or Alan Turing?",
    "answer": 
"""
Are follow up questions needed here: Yes.
Follow up: How old was Muhammad Ali when he died?
Intermediate answer: Muhammad Ali was 74 years old when he died.
Follow up: How old was Alan Turing when he died?
Intermediate answer: Alan Turing was 41 years old when he died.
So the final answer is: Muhammad Ali
"""
  },
  {
    "question": "When was the founder of craigslist born?",
    "answer": 
"""
Are follow up questions needed here: Yes.
Follow up: Who was the founder of craigslist?
Intermediate answer: Craigslist was founded by Craig Newmark.
Follow up: When was Craig Newmark born?
Intermediate answer: Craig Newmark was born on December 6, 1952.
So the final answer is: December 6, 1952
"""
  },
  {
    "question": "Who was the maternal grandfather of George Washington?",
    "answer":
"""
Are follow up questions needed here: Yes.
Follow up: Who was the mother of George Washington?
Intermediate answer: The mother of George Washington was Mary Ball Washington.
Follow up: Who was the father of Mary Ball Washington?
Intermediate answer: The father of Mary Ball Washington was Joseph Ball.
So the final answer is: Joseph Ball
"""
  },
  {
    "question": "Are both the directors of Jaws and Casino Royale from the same country?",
    "answer":
"""
Are follow up questions needed here: Yes.
Follow up: Who is the director of Jaws?
Intermediate Answer: The director of Jaws is Steven Spielberg.
Follow up: Where is Steven Spielberg from?
Intermediate Answer: The United States.
Follow up: Who is the director of Casino Royale?
Intermediate Answer: The director of Casino Royale is Martin Campbell.
Follow up: Where is Martin Campbell from?
Intermediate Answer: New Zealand.
So the final answer is: No
"""
  }
]

example_prompt = PromptTemplate(input_variables=["question", "answer"], template="Question: {question}\n{answer}")
print(example_prompt.format(**examples[0]))
# 这里输出的是第一个范例

prompt = FewShotPromptTemplate(
    examples=examples, 
    example_prompt=example_prompt, 
    suffix="Question: {input}", 
    input_variables=["input"]
)
print(prompt.format(input="Who was the father of Mary Ball Washington?"))

from langchain.chat_models import ChatOpenAI
from langchain import PromptTemplate, LLMChain
from langchain.prompts.chat import (
    ChatPromptTemplate,
    SystemMessagePromptTemplate,
    AIMessagePromptTemplate,
    HumanMessagePromptTemplate,
)
from langchain.schema import AIMessage, HumanMessage, SystemMessage

os.environ["ZHIPUAI_API_KEY"] = ""
chat = ChatZhipuAI(
    model="glm-4",
    temperature=0.5,
)

template = "You are a helpful assistant that translates english to pirate."
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
example_human = HumanMessagePromptTemplate.from_template("Hi")
example_ai = AIMessagePromptTemplate.from_template("Argh me mateys")
human_template = "{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)

chat_prompt = ChatPromptTemplate.from_messages(
    [system_message_prompt, example_human, example_ai, human_message_prompt]
)
chain = LLMChain(llm=chat, prompt=chat_prompt)
# get a chat completion from the formatted messages
chain.run("I love programming.")

from datetime import datetime

def _get_datetime():
    now = datetime.now()
    return now.strftime("%m/%d/%Y, %H:%M:%S")

prompt = PromptTemplate(
    template="Tell me a {adjective} joke about the day {date}", 
    input_variables=["adjective", "date"]
);
partial_prompt = prompt.partial(date=_get_datetime)
print(partial_prompt.format(adjective="funny"))

prompt = PromptTemplate(
    template="Tell me a {adjective} joke about the day {date}", 
    input_variables=["adjective"],
    partial_variables={"date": _get_datetime}
);
print(prompt.format(adjective="funny"))

from langchain.prompts.pipeline import PipelinePromptTemplate
from langchain.prompts.prompt import PromptTemplate

full_template = """{introduction}

{example}

{start}"""
full_prompt = PromptTemplate.from_template(full_template)

introduction_template = """You are impersonating {person}."""
introduction_prompt = PromptTemplate.from_template(introduction_template)

example_template = """Here's an example of an interaction: 

Q: {example_q}
A: {example_a}"""
example_prompt = PromptTemplate.from_template(example_template)

start_template = """Now, do this for real!

Q: {input}
A:"""
start_prompt = PromptTemplate.from_template(start_template)

input_prompts = [
    ("introduction", introduction_prompt),
    ("example", example_prompt),
    ("start", start_prompt)
]
pipeline_prompt = PipelinePromptTemplate(final_prompt=full_prompt, pipeline_prompts=input_prompts)

pipeline_prompt.input_variables

print(pipeline_prompt.format(
    person="Elon Musk",
    example_q="What's your favorite car?",
    example_a="Telsa",
    input="What's your favorite social media site?"
))

# 所有的提示都通过`load_prompt`函数加载。
from langchain.prompts import load_prompt

prompt = load_prompt("simple_prompt.json")
print(prompt.format(adjective="funny", content="chickens"))

{

    "_type": "prompt",

    "input_variables": ["adjective", "content"],

    "template": "Tell me a {adjective} joke about {content}."

}

from langchain_community.chat_models import ChatZhipuAI
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
import os

os.environ["ZHIPUAI_API_KEY"] = ""
chat = ChatZhipuAI(
    model="glm-4",
    temperature=0.5,
)

messages = [
    # AIMessage(content="Hi."),
    # SystemMessage(content="你是一个数学家,只会回答数学问题."),
    HumanMessage(content="已知一张桌子的价钱是一把椅子的10倍，又知一张桌子比一把椅子多288元，一张桌子和一把椅子各多少元？"),
]
response = chat.invoke(messages)
print(response.content) 

chat(messages)

batch_messages = [
    [
        SystemMessage(content="You are a helpful assistant that translates English to French."),
        HumanMessage(content="I love programming.")
    ],
    [
        SystemMessage(content="You are a helpful assistant that translates English to French."),
        HumanMessage(content="I love artificial intelligence.")
    ],
]
chat.generate(batch_messages)

from langchain import PromptTemplate
from langchain.prompts.chat import (
    ChatPromptTemplate,
    SystemMessagePromptTemplate,
    AIMessagePromptTemplate,
    HumanMessagePromptTemplate,
)

template="You are a helpful assistant that translates {input_language} to {output_language}."
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template="{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)

chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])

# get a chat completion from the formatted messages
chat(chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.").to_messages())

from langchain.cache import InMemoryCache
import langchain
from langchain_community.chat_models import ChatZhipuAI
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
import os

os.environ["ZHIPUAI_API_KEY"] = ""
chat = ChatZhipuAI(
    model="glm-4",
    temperature=0.5,
)

langchain.llm_cache = InMemoryCache()


# The first time, it is not yet in cache, so it should take longer
# 二次调用时速度加快很多。
chat.predict("Tell me a farker")

# We can do the same thing with a SQLite cache
from langchain.cache import SQLiteCache

langchain.llm_cache = SQLiteCache(database_path=".langchain.db")

# The first time, it is not yet in cache, so it should take longer
chat.predict("Tell me a joke")

from langchain.chat_models import ChatOpenAI
from langchain.schema import (
    HumanMessage,
)


from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler

from langchain_community.chat_models import ChatZhipuAI
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
import os

os.environ["ZHIPUAI_API_KEY"] = ""
chat = ChatZhipuAI(
    model="glm-4",
    temperature=0.5,
    streaming=True, 
    callbacks=[StreamingStdOutCallbackHandler()]
)
resp = chat([HumanMessage(content="Write me a song about sparkling water.")])

from langchain.output_parsers import CommaSeparatedListOutputParser
from langchain.prompts import PromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplate
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI

output_parser = CommaSeparatedListOutputParser()

format_instructions = output_parser.get_format_instructions()

prompt = PromptTemplate(
    template="List five {subject}.\n{format_instructions}",
    input_variables=["subject"],
    partial_variables={"format_instructions": format_instructions}
)


os.environ["ZHIPUAI_API_KEY"] = ""
chat = ChatZhipuAI(
    model="glm-4",
    temperature=0.5,
)

_input = prompt.format(subject="ice cream flavors")
output = chat.invoke(_input)
output_parser.parse(output.content)

from langchain.prompts import PromptTemplate
from langchain.output_parsers import DatetimeOutputParser
from langchain.chains import LLMChain
from langchain.llms import OpenAI

output_parser = DatetimeOutputParser()
template = """Answer the users question:

{question}

{format_instructions}"""
prompt = PromptTemplate.from_template(
    template,
    partial_variables={"format_instructions": output_parser.get_format_instructions()},
)

os.environ["ZHIPUAI_API_KEY"] = ""
chat = ChatZhipuAI(
    model="glm-4",
    temperature=0.5,
)

chain = LLMChain(prompt=prompt, llm=chat)
output = chain.run("around when was bitcoin founded?")
output

from langchain.output_parsers.enum import EnumOutputParser

from enum import Enum


class Colors(Enum):
    RED = "red"
    GREEN = "green"
    BLUE = "blue"

parser = EnumOutputParser(enum=Colors)
parser.parse("red")

# Can handle spaces
parser.parse(" green")

# And new lines
parser.parse("blue\n")

pip install feast

feast init my_feature_repo
cd my_feature_repo/feature_repo

from feast import Entity, Feature, FeatureView, FileSource, ValueType

# 定义数据源
driver_hourly_stats = FileSource(
    path="/path/to/driver_stats.parquet",
    event_timestamp_column="event_timestamp",
    created_timestamp_column="created",
)

# 定义实体
driver = Entity(name="driver_id", value_type=ValueType.INT64, description="driver id")

# 定义特征视图
driver_hourly_stats_view = FeatureView(
    name="driver_hourly_stats",
    entities=["driver_id"],
    ttl=timedelta(seconds=86400 * 1),
    features=[
        Feature(name="conv_rate", dtype=ValueType.FLOAT),
        Feature(name="acc_rate", dtype=ValueType.FLOAT),
        Feature(name="avg_daily_trips", dtype=ValueType.INT64),
    ],
    online=True,
    batch_source=driver_hourly_stats,
    tags={},
)

feast apply

feast ui

CURRENT_TIME=$(date -u +"%Y-%m-%dT%H:%M:%S")
feast materialize-incremental $CURRENT_TIME

from feast import FeatureStore

store = FeatureStore(repo_path=".")

feature_vector = store.get_online_features(
    features=[
        'driver_hourly_stats:conv_rate',
        'driver_hourly_stats:acc_rate',
        'driver_hourly_stats:avg_daily_trips'
    ],
    entity_rows=[{"driver_id": 1001}]
).to_dict()

print(feature_vector)