Initial commit: Starting the LangChain & Vector Databases in Producti…

…on course with Activeloop

01_basic.py

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+from langchain.llms import OpenAI
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# Call the LLM
+llm = OpenAI(model="text-davinci-003", temperature=0.9)
+
+# The Prompt
+prompt = "Suggest a personalized workout routine for someone looking to improve cardiovascular endurance and prefers outdoor activities."
+
+# pass the prompt to the LLM
+print(llm(prompt))

02_chain.py

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+from langchain.prompts import PromptTemplate
+from langchain.llms import OpenAI
+from langchain.chains import LLMChain
+from dotenv import load_dotenv
+
+load_dotenv()
+
+llm = OpenAI(model="text-davinci-003", temperature=0.9)
+prompt = PromptTemplate(
+    input_variables=["product"],
+    template="What is a good name for a company that makes {product}?",
+)
+
+chain = LLMChain(llm=llm, prompt=prompt, verbose=True)
+
+product = chain.run(input("Enter your product name: "))
+
+# Run the chain only specifying the input variable.
+print(product)

03_track_usage.py

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+from langchain.llms import OpenAI
+from langchain.callbacks import get_openai_callback
+
+llm = OpenAI(model_name="text-davinci-003", n=2, best_of=2)
+
+with get_openai_callback() as cb:
+    result = llm("Tell me a joke")
+    cost = cb.total_cost
+    print("$",round(cost, 5)) 

04_few_short.py

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+from langchain import PromptTemplate
+from langchain import FewShotPromptTemplate
+from langchain.chat_models import ChatOpenAI
+from langchain import LLMChain
+
+# create our examples dictionery
+examples = [
+    {
+        "query": "What's the weather like?",
+        "answer": "It's raining cats and dogs, better bring an umbrella!"
+    }, {
+        "query": "How old are you?",
+        "answer": "Age is just a number, but I'm timeless."
+    }
+]
+
+# create an example template
+example_template = """
+User: {query}
+AI: {answer}
+"""
+
+# create a prompt example from above template
+example_prompt = PromptTemplate(
+    input_variables=["query", "answer"],
+    template=example_template
+)
+
+# now break our previous prompt into a prefix and suffix
+# the prefix is our instructions
+prefix = """The following are excerpts from conversations with an AI
+assistant. The assistant is known for its humor and wit, providing
+entertaining and amusing responses to users' questions. Here are some
+examples:
+"""
+# and the suffix our user input and output indicator
+suffix = """
+User: {query}
+AI: """
+
+# now create the fe w-shot prompt template
+few_shot_prompt_template = FewShotPromptTemplate(
+    examples=examples,
+    example_prompt=example_prompt,
+    prefix=prefix,
+    suffix=suffix,
+    input_variables=["query"],
+    example_separator="\n\n"
+)
+
+# load the model
+chat = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.9)
+
+chain = LLMChain(llm=chat, prompt=few_shot_prompt_template, verbose=True)
+
+ask_question = chain.run(input("Ask your question: "))
+
+print(ask_question)

05_question_answering.py

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+from langchain import PromptTemplate
+from langchain import HuggingFaceHub, LLMChain
+from dotenv import load_dotenv 
+
+load_dotenv()
+
+template = """Question: {question}
+
+Answer: """
+prompt = PromptTemplate(
+        template=template,
+        input_variables=['question']
+        )   
+
+# user question
+question =input("")
+
+# initialize Hub LLM
+hub_llm = HuggingFaceHub(
+        repo_id='google/flan-t5-large',
+    model_kwargs={'temperature':0}
+)
+
+# create prompt template > LLM chain
+llm_chain = LLMChain(
+    prompt=prompt,
+    llm=hub_llm
+)
+
+# ask the user question about the capital of France
+print(llm_chain.run(question)) 

06_text_summarization.py

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+from langchain.chat_models import ChatOpenAI
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+
+llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
+
+summarization_template = "Summarize the following text to one sentence: {text}"
+summarization_prompt = PromptTemplate(input_variables=["text"], template=summarization_template)
+summarization_chain = LLMChain(llm=llm, prompt=summarization_prompt, verbose=True)
+
+text = "LangChain provides many modules that can be used to build language model applications. Modules can be combined to create more complex applications, or be used individually for simple applications. The most basic building block of LangChain is calling an LLM on some input. Let’s walk through a simple example of how to do this. For this purpose, let’s pretend we are building a service that generates a company name based on what the company makes."
+summarized_text = summarization_chain.predict(text=text)
+
+print(summarized_text)

07_movies_finder.py

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+from langchain.chat_models import ChatOpenAI
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    SystemMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+)
+
+# Before executing the following code, make sure to have
+# your OpenAI key saved in the “OPENAI_API_KEY” environment variable.
+chat = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
+
+template = "You are an assistant that helps users find information about movies."
+system_message_prompt = SystemMessagePromptTemplate.from_template(template)
+human_template = "Find information about the movie {movie_title}."
+human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
+
+chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
+
+response = chat(chat_prompt.format_prompt(movie_title="50 shades of grey").to_messages())
+
+print(response.content)

08_chat_models.py

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+from langchain.chat_models import ChatOpenAI
+from langchain.schema import (
+  HumanMessage,
+  SystemMessage
+)
+
+chat = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
+
+messages = [
+	SystemMessage(content="You are a helpful assistant that translates English to French."),
+	HumanMessage(content="Translate the following sentence: I love programming.")
+]
+
+chat(messages)

09_news_summarizer.py

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+# Import necessary libraries
+import json 
+from dotenv import load_dotenv
+import requests
+from newspaper import Article
+from langchain.schema import HumanMessage
+from langchain.chat_models import ChatOpenAI
+
+# Load environment variables
+load_dotenv()
+
+# Set headers for requests
+headers = {
+    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.82 Safari/537.36'
+}
+
+# Specify the URL of the article to summarize
+article_url = "https://www.artificialintelligence-news.com/2022/01/25/meta-claims-new-ai-supercomputer-will-set-records/"
+
+# Create a session
+session = requests.Session()
+
+# Fetch the article
+try:
+    response = session.get(article_url, headers=headers, timeout=10)
+
+    if response.status_code == 200:
+        article = Article(article_url)
+        article.download()
+        article.parse()
+
+        # print(f"Title: {article.title}")
+        # print(f"Text: {article.text}")
+
+    else:
+        print(f"Failed to fetch article at {article_url}")
+except Exception as e:
+    print(f"Error occurred while fetching article at {article_url}: {e}")
+
+# Load the model
+chat = ChatOpenAI(model_name="gpt-3.5-turbo-16k", temperature=0)
+
+# Prepare the prompt
+template = """You are an advanced AI assistant that summarizes online articles into bulleted lists.
+
+Here's the article you need to summarize.
+
+==================
+Title: {article_title}
+
+{article_text}
+==================
+
+Now, provide a summarized version of the article in a bulleted list format.
+"""
+
+# format prompt
+prompt = template.format(article_title=article.title, article_text=article.text)
+
+
+# Generate summary
+messages = [HumanMessage(content=prompt)]
+summary = chat(messages)
+print(summary.content)

10_gpt4all.py

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+# Import modules
+from langchain.llms import GPT4All
+from langchain import PromptTemplate, LLMChain
+# from langchain.callbacks.base import CallbackManager
+from langchain.callbacks.manager import CallbackManager
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+
+template = """Question: {question}
+
+Answer: Let's think step by step."""
+prompt = PromptTemplate(template=template, input_variables=["question"])
+
+callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
+llm = GPT4All(model="E:/Backup/Documents/Softwares/GPT4All/Models/ggml-wizardLM-7B.q4_2.bin", callback_manager=callback_manager, verbose=True)
+llm_chain = LLMChain(prompt=prompt, llm=llm)
+
+question = "What happens when it rains somewhere?"
+llm_chain.run(question)

11_chain_prompting.py

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+from langchain import PromptTemplate, LLMChain
+from langchain.llms import OpenAI
+
+# Initialize LLM
+llm = OpenAI(model_name="text-davinci-003", temperature=0)
+
+# Prompt 1
+template_question = """What is the name of the famous scientist who developed the theory of general relativity?
+Answer: """
+prompt_question = PromptTemplate(
+    template=template_question, 
+    input_variables=[])
+
+# Prompt 2
+template_fact = """Provide a brief description of {scientist}'s theory of general relativity.
+Answer: """
+prompt_fact = PromptTemplate(
+    input_variables=["scientist"], 
+    template=template_fact)
+
+# Create the LLMChain for the first prompt
+chain_question = LLMChain(llm=llm, prompt=prompt_question)
+
+# Run the LLMChain for the first prompt with an empty dictionary
+response_question = chain_question.run({})
+
+# Extract the scientist's name from the response
+scientist = response_question.strip()
+
+# Create the LLMChain for the second prompt
+chain_fact = LLMChain(llm=llm, prompt=prompt_fact)
+
+# Input data for the second prompt
+input_data = {"scientist": scientist}
+
+# Run the LLMChain for the second prompt
+response_fact = chain_fact.run(input_data)
+
+print("Scientist:", scientist)
+print("Fact:", response_fact)