Memuat dan membagi berkas kode

Retrieval Augmented Generation (RAG) dengan LangChain

Meri Nova

Machine Learning Engineer

Lebih banyak pemuat dokumen...

Pilihan berbagai format berkas.

Markdown mentah yang digunakan untuk membuat berkas README di repositori GitHub LangChain.

Markdown yang dirender dari berkas README.md GitHub LangChain.

Memuat berkas Markdown (.md)

from langchain_community.document_loaders import UnstructuredMarkdownLoader


loader = UnstructuredMarkdownLoader("README.md")

markdown_content = loader.load()
print(markdown_content[0])

Document(page_content='# Discord Text Classification ![Python Version](https...'
         metadata={'source': 'README.md'})

Memuat berkas Python (.py)

from abc import ABC, abstractmethod

class LLM(ABC):
  @abstractmethod
  def complete_sentence(self, prompt):
    pass

...

Diintegrasikan ke aplikasi RAG untuk menulis/memperbaiki kode, membuat dokumentasi, dll.
Impor, kelas, fungsi, dll.

from langchain_community.document_loaders \
    import PythonLoader

loader = PythonLoader('chatbot.py')


python_data = loader.load()
print(python_data[0])

Document(page_content='from abc import ABC, ...

class LLM(ABC):
  @abstractmethod
...',
metadata={'source': 'chatbot.py'})

Membagi berkas kode

python_splitter = RecursiveCharacterTextSplitter(
    chunk_size=150, chunk_overlap=10
)


chunks = python_splitter.split_documents(python_data)
for i, chunk in enumerate(chunks[:3]):
    print(f"Chunk {i+1}:\n{chunk.page_content}\n")

Chunk 1:
from abc import ABC, abstractmethod

class LLM(ABC):
  @abstractmethod
  def complete_sentence(self, prompt):
    pass

Chunk 2:
class OpenAI(LLM):
  def complete_sentence(self, prompt):
    return prompt + " ... OpenAI end of sentence."

class Anthropic(LLM):

Chunk 3:
def complete_sentence(self, prompt):
    return prompt + " ... Anthropic end of sentence."

Membagi berdasarkan bahasa

separators
- ["\n\n", "\n", " ", ""]
- ["\nclass ", "\ndef ", "\n\tdef ", "\n\n", " ", ""]

from langchain_text_splitters import RecursiveCharacterTextSplitter, Language

python_splitter = RecursiveCharacterTextSplitter.from_language(

    language=Language.PYTHON, chunk_size=150, chunk_overlap=10

)


chunks = python_splitter.split_documents(data)


for i, chunk in enumerate(chunks[:3]):
    print(f"Chunk {i+1}:\n{chunk.page_content}\n")

Chunk 1:
from abc import ABC, abstractmethod

Chunk 2:
class LLM(ABC):
  @abstractmethod
  def complete_sentence(self, prompt):
    pass

Chunk 3:
class OpenAI(LLM):
  def complete_sentence(self, prompt):

Ayo berlatih!

Retrieval Augmented Generation (RAG) dengan LangChain