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