3 ms·

I'm guessing langchain / llamaindex + openai API?

by lxe 4y ago

nextworddev 4y ago
It’s a great mystery why people rave about Langchain library. I found it to be really bloated and verbose syntax makes it hard to understand what it’s doing unless you are digging into the code base. Poorly documented, too
- ukuina 4y ago
  Have you tried Deepset Haystack?
- madmax108 4y ago
  What alternatives exist today? Honestly, I think langchain just fills a void for having a more streamlined "api" for LLM-driven app workflows which is why it's hyped. Plus the fact that they have bindings for both JS and Python makes it easy to get up and running and building custom "agents" for different tasks.
TrapLord_Rhodo 4y ago
lol - Yeah, I could write this in 20 lines. I have it answering a list of questions in an excel from three different 'Frames of mind' by passing three different directory'swith different content in each to get three different responses i can craft together. 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)

I'm guessing langchain / llamaindex + openai API?

by lxe 4y ago

I'm guessing langchain / llamaindex + openai API?

It’s a great mystery why people rave about Langchain library. I found it to be really bloated and verbose syntax makes it hard to understand what it’s doing unless you are digging into the code base. Poorly documented, too

ukuina 4y ago
Have you tried Deepset Haystack?
madmax108 4y ago
What alternatives exist today? Honestly, I think langchain just fills a void for having a more streamlined "api" for LLM-driven app workflows which is why it's hyped. Plus the fact that they have bindings for both JS and Python makes it easy to get up and running and building custom "agents" for different tasks.

TrapLord_Rhodo 4y ago

lol - Yeah, I could write this in 20 lines. I have it answering a list of questions in an excel from three different 'Frames of mind' by passing three different directory'swith different content in each to get three different responses i can craft together. 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)