Upload mohanism_195 (1).py

mohanism_195 (1).py

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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")