3 ms·

Yes - I write cyber security contracts. I feed it proposals i find from solicitation aggregators and it's suprisingly accurate at answering the below questions

Yes - I write cyber security contracts. I feed it proposals i find from solicitation aggregators and it's suprisingly accurate at answering the below questions about 150 page pdfs. *Granted, I don't use this service. I use llama index and AWS to store the vectorized prompt files. Whats the contracting officers email address where the proposal needs to be submitted? What is the Proposal Due Date? What's the full Agency Name? What's a shorter way to say the agency name? What is the RFP number? What type of project are we responding to? Is it a proposal or just a request for information/ Sources Sought Notice? If it is a proposal, simply say proposal. If it is an Request for information or sources sought, say RFI. Are there any vendor upcoming vendor meetings? What is the CALENDAR OF EVENTS? Project Timelines, or deliverables? Can the response be submitted VIA Email? Or is it a sealed bid? What is a description of the scope of work, Scope Of Services or Performance Work Statement? IS the requirement for a Penetration Testing, or Audit Software? How Many Targets are there? Targets consist of Enterprise Systems (Databases, Cloud Enviroments, applications, servers, IP Addresses, Firewalls, network attached storage, and wireless networks ) and Enterprise infrastructure(Switchgears, routers, Modems, HUBS). List the total number of targets by each category. Does it require Social Engineering, Physical Security, Wireless Scanning or Perimieter Security?

syntaxing 4y ago
Is there something that will allow me to run this locally?! This is exactly what I want to do but no clue how to pipe my data into llama. Any pointers will be highly appreciated!
- TrapLord_Rhodo 4y ago
  Locally? It's not really possible to run the model locally, but finetuning on directories is possible. Here's the code: It's runs on three different directorys to give me three different 'answers' using an excel sheet to pull the q's from. def excelGPT(self, dir, excel_file, sheet): #my GPT Key os.environ['OPENAI_API_KEY'] = 'sk- #Working Directory for training # root = root_folder1 = documents1 = SimpleDirectoryReader(root_folder1).load_data() index1 = GPTSimpleVectorIndex(documents1) root_folder2 = documents2 = SimpleDirectoryReader(root_folder2).load_data() index2 = GPTSimpleVectorIndex(documents2) root_folder3 = documents3 = SimpleDirectoryReader(root_folder3).load_data() index3 = GPTSimpleVectorIndex(documents3) file_name = dir + excel_file df = pd.read_excel(file_name, sheet_name=sheet) GSA_answer_array = [] basic_answer_array = [] QA_answer_array = [] df_series = df.iloc[:,0] for i,x in enumerate(df_series): print("This is the index ", i) print(x) GSA_response = index1.query(x) basic_response = index2.query(x) QA_response = index3.query(x) GSA_answer_array.append(str(GSA_response)) basic_answer_array.append(str(basic_response)) QA_answer_array.append(str(QA_response)) self.zip_to_docv2(dir, "Gippie_Response.docx", df_series, GSA_answer_array, basic_answer_array, QA_answer_array)
  - syntaxing 4y ago
    This is awesome! Thank you for the code!
- ukuina 4y ago
  This, so much this! There are so many projects that claim to do this, but end up piping data to OpenAI. Can someone who has managed to get this set up locally send some pointers our way?

syntaxing 4y ago

Is there something that will allow me to run this locally?! This is exactly what I want to do but no clue how to pipe my data into llama. Any pointers will be highly appreciated!

TrapLord_Rhodo 4y ago
Locally? It's not really possible to run the model locally, but finetuning on directories is possible. Here's the code: It's runs on three different directorys to give me three different 'answers' using an excel sheet to pull the q's from. def excelGPT(self, dir, excel_file, sheet): #my GPT Key os.environ['OPENAI_API_KEY'] = 'sk- #Working Directory for training # root = root_folder1 = documents1 = SimpleDirectoryReader(root_folder1).load_data() index1 = GPTSimpleVectorIndex(documents1) root_folder2 = documents2 = SimpleDirectoryReader(root_folder2).load_data() index2 = GPTSimpleVectorIndex(documents2) root_folder3 = documents3 = SimpleDirectoryReader(root_folder3).load_data() index3 = GPTSimpleVectorIndex(documents3) file_name = dir + excel_file df = pd.read_excel(file_name, sheet_name=sheet) GSA_answer_array = [] basic_answer_array = [] QA_answer_array = [] df_series = df.iloc[:,0] for i,x in enumerate(df_series): print("This is the index ", i) print(x) GSA_response = index1.query(x) basic_response = index2.query(x) QA_response = index3.query(x) GSA_answer_array.append(str(GSA_response)) basic_answer_array.append(str(basic_response)) QA_answer_array.append(str(QA_response)) self.zip_to_docv2(dir, "Gippie_Response.docx", df_series, GSA_answer_array, basic_answer_array, QA_answer_array)
- syntaxing 4y ago
  This is awesome! Thank you for the code!
ukuina 4y ago
This, so much this! There are so many projects that claim to do this, but end up piping data to OpenAI. Can someone who has managed to get this set up locally send some pointers our way?