Langchain

Library for accessing different AI models and create agents

from langchain import OpenAI, ChatOpenAI, ConversationChain

llm1 = OpenAI(temperature=0)
# gpt-3.5-turbo also an option
llm2 = ChatOpenAI(temperature=0.9, model_name='gpt-4')
conversation = ConversationChain(llm=llm, verbose=True)

conversation.predict(input="Hi there!")

Prompts

from langchain import PromptTemplate

template = """
I want you to act as a naming consultant for new companies.

Here are some examples of good company names:

- search engine, Google
- social media, Facebook
- video sharing, YouTube

The name should be short, catchy and easy to remember.

What is a good name for a company that makes {product}?
"""

prompt = PromptTemplate(
    input_variables=["product"],
    template=template,
)

chain = LLMChain(llm=llm, prompt=prompt)
chain.run("Interdimensial AI News")

Incontext Learning

from langchain.prompts.example_selector import LengthBasedExampleSelector


# These are a lot of examples of a pretend task of creating antonyms.
examples = [
    {"word": "happy", "antonym": "sad"},
    {"word": "tall", "antonym": "short"},
    {"word": "energetic", "antonym": "lethargic"},
    {"word": "sunny", "antonym": "gloomy"},
    {"word": "windy", "antonym": "calm"},
]

# We'll use the `LengthBasedExampleSelector` to select the examples.
example_selector = LengthBasedExampleSelector(
    # These are the examples is has available to choose from.
    examples=examples, 
    # This is the PromptTemplate being used to format the examples.
    example_prompt=example_prompt, 
    # This is the maximum length that the formatted examples should be.
    # Length is measured by the get_text_length function below.
    max_length=25,
)

# We can now use the `example_selector` to create a `FewShotPromptTemplate`.
dynamic_prompt = FewShotPromptTemplate(
    # We provide an ExampleSelector instead of examples.
    example_selector=example_selector,
    example_prompt=example_prompt,
    prefix="Give the antonym of every input",
    suffix="Word: {input}\nAntonym:",
    input_variables=["input"],
    example_separator="\n\n",
)

chain = LLMChain(llm=llm, prompt=dynamic_prompt, verbose=True)
print(chain.run("God is the good"))
print(chain.run("big and huge and massive and large and gigantic and tall and much much much much much bigger than everything else"))

Other Example

from langchain.prompts.example_selector import MaxMarginalRelevanceExampleSelector
from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings

example_selector = MaxMarginalRelevanceExampleSelector.from_examples(
    # This is the list of examples available to select from.
    examples, 
    # This is the embedding class used to produce embeddings which are used to measure semantic similarity.
    OpenAIEmbeddings(), 
    # This is the VectorStore class that is used to store the embeddings and do a similarity search over.
    FAISS, 
    # This is the number of examples to produce.
    k=2
)
mmr_prompt = FewShotPromptTemplate(
    # We provide an ExampleSelector instead of examples.
    example_selector=example_selector,
    example_prompt=example_prompt,
    prefix="Give the antonym of every input",
    suffix="Input: {adjective}\nOutput:", 
    input_variables=["adjective"],
)

# Input is a feeling, so should select the happy/sad example as the first one
print(mmr_prompt.format(adjective="dead"))

Tools

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

# First, let's load the language model we're going to use to control the agent.
llm = OpenAI(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.
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, llm, agent="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?")