8 changed files with 276 additions and 5 deletions
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import ollama |
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import os |
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import requests |
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from bs4 import BeautifulSoup |
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from IPython.display import Markdown, display |
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MODEL = "llama3.2:3b-instruct-q8_0" |
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messages = [ |
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{"role": "user", "content": "Describe some of the business applications of Generative AI"} |
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] |
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# response = ollama.chat(model=MODEL, messages=messages) |
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# print(response['message']['content']) |
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class Website: |
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def __init__(self, url): |
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""" |
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Create this Website object from the given url using the BeautifulSoup library |
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""" |
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self.url = url |
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response = requests.get(url) |
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soup = BeautifulSoup(response.content, 'html.parser') |
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self.title = soup.title.string if soup.title else "No title found" |
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try: |
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for irrelevant in soup.body(["script", "style", "img", "input"]): |
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irrelevant.decompose() |
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except: |
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pass |
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self.text = soup.body.get_text(separator="\n", strip=True) |
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system_prompt = "You are an assistant that analyzes the contents of a website \ |
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and provides a short summary, ignoring text that might be navigation related. \ |
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Respond in markdown." |
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def user_prompt_for(website): |
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user_prompt = f"You are looking at a website titled {website.title}" |
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user_prompt += "\nThe contents of this website is as follows; \ |
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please provide a short summary of this website in markdown. \ |
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If it includes news or announcements, then summarize these too.\n\n" |
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user_prompt += website.text |
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return user_prompt |
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def messages_for(website): |
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return [ |
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{"role": "system", "content": system_prompt}, |
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{"role": "user", "content": user_prompt_for(website)} |
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] |
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def summarize(url): |
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website = Website(url) |
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response = ollama.chat( |
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model = MODEL, |
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messages = messages_for(website) |
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) |
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return response['message']['content'] |
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def display_summary(url): |
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summary = summarize(url) |
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print(summary) |
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display_summary("https://mike-tupper.com/") |
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This appears to be a press release archive or news feed from Anthropic, a company that specializes in developing large language models and artificial intelligence. Here's a summary of the key points: |
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**Company Overview** |
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* Anthropic is a leader in developing AI models that can understand and generate human-like language. |
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* The company has developed several AI models, including Claude, which is a conversational AI model designed to assist with various tasks. |
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**Product Updates** |
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* Claude: A conversational AI model that can be used for a variety of tasks, including customer service, data enrichment, and more. New versions of Claude have been released regularly, including: |
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+ Claude 2 (available on Amazon Bedrock) |
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+ Claude 3 (features improved performance and new capabilities) |
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+ Claude 3 Haiku (a faster version of Claude 3) |
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+ Claude Pro (an enterprise-level version of Claude) |
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**Partnerships and Collaborations** |
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* Anthropic has partnered with several companies, including: |
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+ Amazon Web Services (AWS) to integrate Claude into AWS's services |
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+ Accenture to develop trusted solutions for enterprises |
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+ BCG (Boston Consulting Group) to expand access to safer AI |
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+ SKT (Samsung Electronics) on a partnership announcement |
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**Industry Recognition** |
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* Anthropic has received recognition from industry leaders and experts, including: |
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+ Dario Amodei's prepared remarks from the AI Safety Summit on Anthropic's Responsible Scaling Policy |
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+ Thoughts on the US Executive Order, G7 Code of Conduct, and Bletchley Park Summit by Anthropic |
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**Safety and Ethics** |
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* Anthropic has emphasized its commitment to safety and ethics in AI development, including: |
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+ Expanding access to safer AI with Amazon |
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+ Releasing a Responsible Scaling Policy |
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+ Aligning on child safety principles |
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**Other News** |
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* Anthropic has also made headlines for other reasons, such as: |
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+ Introducing Claude Android app |
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+ Fine-tuning Claude 3 Haiku in Amazon Bedrock |
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+ Evaluating prompts in the developer console |
@ -0,0 +1,32 @@ |
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Based on the provided webpage contents, here is a summary of the information: |
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**About Anthropic** |
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* Anthropic is a company that develops large-scale AI systems. |
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* Their research teams aim to create safer, steerable, and more reliable models. |
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**Careers** |
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* Anthropic has a careers page that lists open roles across various teams and offices. |
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* The company values direct evidence of ability, such as independent research, blog posts, or open-source software contributions. |
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* They do not require PhDs, degrees, or previous ML experience for technical staff positions. |
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* About half of the technical staff have a PhD, and about half had prior experience in ML. |
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* Anthropic sponsors visas and green cards for eligible candidates. |
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**Interview Process** |
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* Interviews are conducted over Google Meet, with a preference for PST office hours. |
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* Candidates can re-apply after 12 months if they are not successful initially. |
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* The company does not provide feedback on resumes or interviews. |
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**Remote Work** |
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* Anthropic staff typically come to the office regularly, but some may work remotely part-time or full-time. |
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* The company understands that moving can take time and offers a transitional phase for remote workers. |
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**Research** |
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* Anthropic's research teams focus on developing safer, steerable, and more reliable large-scale AI systems. |
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* The company is working at the frontier of AI research and development. |
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Overall, Anthropic appears to be a company that values innovation, expertise, and diversity in its workforce. They prioritize creating safe and reliable AI systems and offer opportunities for career growth and development. |
@ -0,0 +1,134 @@ |
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import ollama |
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import os |
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import requests |
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import json |
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from bs4 import BeautifulSoup |
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from IPython.display import Markdown, display |
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""" |
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Available Models: |
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llama3.3:latest a6eb4748fd29 42 GB 24 hours ago |
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granite3-moe:3b 157f538ae66e 2.1 GB 2 weeks ago |
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granite3-dense:8b 199456d876ee 4.9 GB 2 weeks ago |
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nemotron:70b-instruct-q5_K_M def2cefbe818 49 GB 6 weeks ago |
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llama3.2:3b-instruct-q8_0 e410b836fe61 3.4 GB 7 weeks ago |
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llama3.2:latest a80c4f17acd5 2.0 GB 2 months ago |
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reflection:latest 5084e77c1e10 39 GB 3 months ago |
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HammerAI/llama-3.1-storm:latest 876631929cf6 8.5 GB 3 months ago |
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granite-code:34b 4ce00960ca84 19 GB 3 months ago |
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llama3.1:8b 91ab477bec9d 4.7 GB 3 months ago |
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llama3.1-Q8-8b:latest 3d41179680d6 8.5 GB 3 months ago |
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nomic-embed-text:latest 0a109f422b47 274 MB 3 months ago |
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rjmalagon/gte-qwen2-7b-instruct-embed-f16:latest a94ce5b37c1c 15 GB 3 months ago |
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llama3:70b-instruct-q5_K_M 4e84a5514862 49 GB 3 months ago |
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llama3:8b 365c0bd3c000 4.7 GB 3 months ago |
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mistral-nemo:12b-instruct-2407-q8_0 b91eec34730f 13 GB 3 months ago |
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""" |
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MODEL = "llama3.3" |
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messages = [ |
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{"role": "user", "content": "Describe some of the business applications of Generative AI"} |
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] |
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# response = ollama.chat(model=MODEL, messages=messages) |
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# print(response['message']['content']) |
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class Website: |
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""" |
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A utility class to represent a website that we have scraped, now with links |
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""" |
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url: str |
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title: str |
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body: str |
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links: list[str] |
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text: str |
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def __init__(self, url): |
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self.url = url |
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response = requests.get(url) |
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self.body = response.content |
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soup = BeautifulSoup(self.body, 'html.parser') |
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self.title = soup.title.string if soup.title else "No title found" |
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if soup.body: |
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try: |
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for irrelevant in soup.body(["script", "style", "img", "input"]): |
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irrelevant.decompose() |
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self.text = soup.body.get_text(separator="\n", strip=True) |
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except: |
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pass |
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else: |
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self.text = "" |
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links = [link.get('href') for link in soup.find_all('a')] |
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self.links = [link for link in links if link] |
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def get_contents(self): |
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return f"Webpage Title:\n{self.title}\nWebpage Contents:\n{self.text}\n\n" |
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link_system_prompt = "You are provided with a list of links found on a webpage. \ |
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You are able to decide which of the links would be most relevant to include in a brochure about the company, \ |
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such as links to an About page, or a Company page, or Careers/Jobs pages.\n" |
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link_system_prompt += "You should respond in JSON as in this example:" |
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link_system_prompt += """ |
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{ |
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"links": [ |
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{"type": "about page", "url": "https://full.url/goes/here/about"}, |
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{"type": "careers page": "url": "https://another.full.url/careers"} |
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] |
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} |
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""" |
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def get_links_user_prompt(website): |
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user_prompt = f"Here is the list of links on the website of {website.url} - " |
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user_prompt += "please decide which of these are relevant web links for a brochure about the company, respond with the full https URL in JSON format. \ |
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Do not include Terms of Service, Privacy, email links.\n" |
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user_prompt += "Links (some might be relative links):\n" |
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user_prompt += "\n".join(website.links) |
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return user_prompt |
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def get_links(url): |
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website = Website(url) |
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response = ollama.chat( |
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model=MODEL, |
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messages=[ |
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{"role": "system", "content": link_system_prompt}, |
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{"role": "user", "content": get_links_user_prompt(website)} |
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], |
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format="json" |
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) |
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result = response['message']['content'] |
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return json.loads(result) |
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def get_all_details(url): |
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result = "Landing page:\n" |
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result += Website(url).get_contents() |
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links = get_links(url) |
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# print("Found links:", links) |
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for link in links["links"]: |
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result += f"\n\n{link['type']}\n" |
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result += Website(link["url"]).get_contents() |
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return result |
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system_prompt = "You are an assistant that analyzes the contents of several relevant pages from a company website \ |
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and creates a short professional sales brochure about the company for prospective customers, investors and recruits. Respond \ |
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in markdown. Include details of company culture, customers and careers/jobs if you have the information." |
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def get_brochure_user_prompt(company_name, url): |
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user_prompt = f"You are looking at a company called: {company_name}\n" |
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user_prompt += f"Here are the contents of its landing page and other relevant pages; use this information to build a short brochure of the company in markdown.\n" |
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user_prompt += get_all_details(url) |
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user_prompt = user_prompt[:20000] # Truncate if more than 5,000 characters |
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return user_prompt |
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def create_brochure(company_name, url): |
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response = ollama.chat( |
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model=MODEL, |
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messages=[ |
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{"role": "system", "content": system_prompt}, |
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{"role": "user", "content": get_brochure_user_prompt(company_name, url)} |
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], |
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) |
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result = response['message']['content'] |
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print(result) |
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create_brochure("Anthropic", "https://anthropic.com") |
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Reference in new issue