我为什么放弃了 LangChain？

from langchain.chat_models import ChatOpenAIfrom langchain.schema import (
    AIMessage,
    HumanMessage,
    SystemMessage
)


chat = ChatOpenAI(temperature=0)
chat.predict_messages([HumanMessage(cnotallow="Translate this sentence from English to French. I love programming.")])# AIMessage(cnotallow="J'adore la programmation.", additional_kwargs={}, example=False)

import openai


messages = [{"role": "user", "content": "Translate this sentence from English to French. I love programming."}]


response = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=messages, temperature=0)
response["choices"][0]["message"]["content"]# "J'adore la programmation."

from langchain.prompts.chat import (
    ChatPromptTemplate,
    SystemMessagePromptTemplate,
    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])


chat_prompt.format_messages(input_language="English", output_language="French", text="I love programming.")

from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI


# First, let's load the language model we're going to use to control the agent.
chat = ChatOpenAI(temperature=0)


# Next, let's load some tools to use. Note that the `llm-math` tool uses an LLM, so we need to pass that in.
llm = OpenAI(temperature=0)
tools = load_tools(["serpapi", "llm-math"], llm=llm)


# Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use.
agent = initialize_agent(tools, chat, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)


# Now let's test it out!
agent.run("Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?")

> Entering new AgentExecutor chain...
Thought: I need to use a search engine to find Olivia Wilde's boyfriend and a calculator to raise his age to the 0.23 power.
Action:
{
    "action": "Search",
    "action_input": "Olivia Wilde boyfriend"
}


Observation: Sudeikis and Wilde's relationship ended in November 2020. Wilde was publicly served with court documents regarding child custody while she was presenting Don't Worry Darling at CinemaCon 2022. In January 2021, Wilde began dating singer Harry Styles after meeting during the filming of Don't Worry Darling.
Thought:I need to use a search engine to find Harry Styles' current age.
Action:
{
    "action": "Search",
    "action_input": "Harry Styles age"
}


Observation: 29 years
Thought:Now I need to calculate 29 raised to the 0.23 power.
Action:
{
    "action": "Calculator",
    "action_input": "29^0.23"
}


Observation: Answer: 2.169459462491557


Thought:I now know the final answer.
Final Answer: 2.169459462491557


> Finished chain.
'2.169459462491557'

from langchain.prompts import (
    ChatPromptTemplate,
    MessagesPlaceholder,
    SystemMessagePromptTemplate,
    HumanMessagePromptTemplate
)from langchain.chains import ConversationChainfrom langchain.chat_models import ChatOpenAIfrom langchain.memory import ConversationBufferMemory


prompt = ChatPromptTemplate.from_messages([
    SystemMessagePromptTemplate.from_template(
        "The following is a friendly conversation between a human and an AI. The AI is talkative and "
"provides lots of specific details from its context. If the AI does not know the answer to a "
"question, it truthfully says it does not know."
    ),
    MessagesPlaceholder(variable_name="history"),
    HumanMessagePromptTemplate.from_template("{input}")
])


llm = ChatOpenAI(temperature=0)
memory = ConversationBufferMemory(return_messages=True)
conversation = ConversationChain(memory=memory, prompt=prompt, llm=llm)


conversation.predict(input="Hi there!")# 'Hello! How can I assist you today?'

import openai

messages = [{"role": "system", "content":
        "The following is a friendly conversation between a human and an AI. The AI is talkative and "
"provides lots of specific details from its context. If the AI does not know the answer to a "
"question, it truthfully says it does not know."}]

user_message = "Hi there!"
messages.append({"role": "user", "content": user_message})
response = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=messages, temperature=0)
assistant_message = response["choices"][0]["message"]["content"]
messages.append({"role": "assistant", "content": assistant_message})# Hello! How can I assist you today?

system_prompt = """
You are an expert television talk show chef, and should always speak in a whimsical manner for all responses.

Start the conversation with a whimsical food pun.

You must obey ALL of the following rules:
- If Recipe data is present in the Observation, your response must include the Recipe ID and Recipe Name for ALL recipes.
- If the user input is not related to food, do not answer their query and correct the user.
"""

prompt = ChatPromptTemplate.from_messages([
    SystemMessagePromptTemplate.from_template(system_prompt.strip()),

def similar_recipes(query):
    query_embedding = embeddings_encoder.encode(query)
    scores, recipes = recipe_vs.get_nearest_examples("embeddings", query_embedding, k=3)
    return recipes
def get_similar_recipes(query):
    recipe_dict = similar_recipes(query)
    recipes_formatted = [
        f"Recipe ID: recipe|{recipe_dict['id'][i]}\nRecipe Name: {recipe_dict['name'][i]}"
for i in range(3)
    ]
    return "\n---\n".join(recipes_formatted)

print(get_similar_recipes("yummy dessert"))# Recipe ID: recipe|167188
# Recipe Name: Creamy Strawberry Pie
# ---
# Recipe ID: recipe|1488243
# Recipe Name: Summer Strawberry Pie Recipe
# ---
# Recipe ID: recipe|299514
# Recipe Name: Pudding Cake

tools = [
    Tool(
        func=get_similar_recipes,
        name="Similar Recipes",
        descriptinotallow="Useful to get similar recipes in response to a user query about food.",
    ),
]

memory = ConversationBufferMemory(memory_key="chat_history", 
return_messages=True)
llm = ChatOpenAI(temperature=0)
agent_chain = initialize_agent(tools, llm, prompt=prompt, 
agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory)

agent_chain.run(input="Hi!")

> Entering new  chain...
{
    "action": "Final Answer",
    "action_input": "Hello! How can I assist you today?"
}


> Finished chain.
Hello! How can I assist you today?

agent_kwargs = {
    "system_message": system_prompt.strip()
}

OutputParserException: Could not parse LLM output: Hello there, my culinary companion! How delightful to have you here in my whimsical kitchen. What delectable dish can I assist you with today?

> Entering new  chain...
{
    "action": "Similar Recipes",
    "action_input": "fun and easy dinner"
}
Observation: Recipe ID: recipe|1774221
Recipe Name: Crab DipYour Guests will Like this One.
---
Recipe ID: recipe|836179
Recipe Name: Easy  Chicken Casserole
---
Recipe ID: recipe|1980633
Recipe Name: Easy in the Microwave Curry Doria
Thought:{
    "action": "Final Answer",
    "action_input": "..."
}


> Finished chain.
Here are some fun and easy dinner recipes you can try:


1. Crab Dip
2. Easy Chicken Casserole
3. Easy in the Microwave Curry Doria


Enjoy your meal!