velotiotech

#include <stdio.h>
void print_string(char *str) {
  int i = 0;
  while (str[i]) {
    printf("%c",str[i]);
    i++;
  }
}
int main() {
  char location[20] = "The 1st location\n";

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GeometryReader { geometry in
      ScrollView(.vertical, showsIndicators: false) {
        VStack(alignment: .leading) {
          let rectangleHeight = geometry.size.height - contentSize.height
          if contentSize != .zero, rectangleHeight > 0 {
            Rectangle()
              .frame(height: rectangleHeight)
              .foregroundColor(.clear)
          }
          if let imageURL = viewModel.getImageURL() {

velotiotech

const marvel = {};
const ironMan = { key: 'arcReactor' };
const captainAmerica = { key: 'superSoldierSerum' };

marvel[ironMan] = 2008;
marvel[captainAmerica] = 2011;

console.log(marvel[ironMan]);

velotiotech

def summarize_pdf(file_path):
    split_docs = text_splitter.split_documents(chunks(file_path))
    return map_reduce_chain.run(split_docs)

result_sumary=summarize_pdf(file_path)
print(result_summary)

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map_reduce_chain = MapReduceDocumentsChain(
    llm_chain=map_chain,
    reduce_documents_chain=reduce_documents_chain,
    document_variable_name="docs",
    return_intermediate_steps=False,
)

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combine_documents_chain = StuffDocumentsChain(
    llm_chain=reduce_chain, document_variable_name="doc_summaries"
)
reduce_documents_chain = ReduceDocumentsChain(
    combine_documents_chain=combine_documents_chain,
    collapse_documents_chain=combine_documents_chain,
    token_max=5000,
)

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map_template = """The following is a set of documents
{docs}
Based on this list of docs, summarised into meaningful
Helpful Answer:"""

map_prompt = PromptTemplate.from_template(map_template)
map_chain = LLMChain(llm=llm, prompt=map_prompt)

reduce_template = """The following is set of summaries:
{doc_summaries}

velotiotech

from langchain.chains.mapreduce import MapReduceChain
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains import ReduceDocumentsChain, MapReduceDocumentsChain
from langchain import PromptTemplate
from langchain.chains import LLMChain
from langchain.chains.combine_documents.stuff import StuffDocumentsChain

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from langchain.document_loaders import PyPDFLoader
def chunks(pdf_file_path):
    loader = PyPDFLoader(pdf_file_path)
    docs = loader.load_and_split()
    return docs

velotiotech

from langchain.text_splitter import CharacterTextSplitter
text_splitter = CharacterTextSplitter.from_tiktoken_encoder(
    chunk_size=1000, chunk_overlap=120
)