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# -*- coding: utf-8 -*-
"""mohanism.195
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1AvIdAQmhCWUUe6rT9sck2gBGkecNCjEc
"""
!pip install dotenv
from dotenv import load_dotenv,find_dotenv
load_dotenv(find_dotenv())
from langchain.llns import OpenAI
llm = OpenAI(model_name="text-davinci-003")
llm("explain large language models in one sentence")
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage
)
from langchain.chat_models import ChatOpenAI
chat = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.3)
messages = (
SystemMessage(content="You are an expert data scientist"),
HumanMessage(content="Write a Python script that trains a neural network on simulated data ")
)
response=chat(messages)
print(response.content,ends="\n")
from langchain import PromptTemplate
template = """You are an expert data scientist with an expertise in building deep learning models,
Explain the concept of {concept} in a couple of lines
"""
prompt = PromptTemplate(
input_variable=["concept"],
template=template,
)
prompt
llm(prompt.format(concept="autoencoder"))
from langchain.chains import LLMChain
chain = LLMchain(llm=lln, prompt=prompt)
second_prompt = PromptTemplate(
input_variables=["ml_concept"],
template="Turn the concept description of {ml_concept} and explain it to me like I'm five in 500 words",
)
chain_two = LLMChain(llm=llm, prompt=second_prompt)
from langchain.chains import SimpleSequenttialChain
overall_chain = SimpleSequenttialChain(chains=[chain, chain_two], verbose=True)
explanation = overall_chain.run("autoencoder")
print(explanation)
from langchain.text_splitter importRecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(
chunk_size = 100,
chunk_overlap = 0,
)
text = text_splitter.create_documents([explanation])
text[0].page_content
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings(model_name="ada")
query_result = embeddings.embed_query(texts[0].page_content)
query_result
import os
import pinecome
from langchain.vectors import Pinecone
# initialize pinecome
pinecome.init(
api_key=os.getenv["PINECONE_API_KEY"],
environment(=os.getenv("PINECONE_ENV")
)
index_name = "langchain-quickstart"
search = Pinecone.form_documents(texts, embeddings, index_name=index_name)
query = "What is magical about an autoencoder?"
result = search.similarity_search(query)
result
from langhain.agent.agent_toolkets import create_python_agent
from langchain.tools.python.tool import PythonREPLTool
from langchain.python import PythonREPL
from langchain.llms.openai import OpenAI
agent_executor = create_python_agent(
llm=OpenAI(temperature=0), max_tokens=1000),
verbose=True
)
agent_executor.run("Find the roots (zeros) if the quadratic function 3 * x==2 + 2** - 1") |