63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
import gradio as gr
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import sys
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import os
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from collections.abc import Iterable
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from langchain.document_loaders import PyPDFLoader
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from langchain.document_loaders import Docx2txtLoader
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from langchain.document_loaders import TextLoader
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from langchain.document_loaders import UnstructuredHTMLLoader
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain.text_splitter import CharacterTextSplitter
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from langchain.chains import RetrievalQA
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from langchain.llms import Ollama
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ollama = Ollama(base_url='http://localhost:11434',
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#model="codellama")
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#model="starcoder")
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model="llama2")
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docsUrl = "/home/user/dev/docs"
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documents = []
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for file in os.listdir(docsUrl):
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if file.endswith(".pdf"):
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pdf_path = docsUrl + "/" + file
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loader = PyPDFLoader(pdf_path)
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documents.extend(loader.load())
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elif file.endswith('.docx') or file.endswith('.doc'):
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doc_path = docsUrl + "/" + file
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loader = Docx2txtLoader(doc_path)
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documents.extend(loader.load())
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elif file.endswith('.txt') or file.endswith('.kt') or file.endswith('.json'):
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text_path = docsUrl + "/" + file
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loader = TextLoader(text_path)
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documents.extend(loader.load())
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elif file.endswith('.html') or file.endswith('.htm'):
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text_path = docsUrl + "/" + file
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loader = UnstructuredHTMLLoader(text_path)
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documents.extend(loader.load())
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text_splitter = CharacterTextSplitter(chunk_size=3500, chunk_overlap=20)
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all_splits = text_splitter.split_documents(documents)
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from langchain.embeddings import GPT4AllEmbeddings
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from langchain.vectorstores import Chroma
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vectorstore = Chroma.from_documents(documents=all_splits, embedding=GPT4AllEmbeddings())
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def greet(question):
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docs = vectorstore.similarity_search(question)
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len(docs)
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qachain=RetrievalQA.from_chain_type(ollama, retriever=vectorstore.as_retriever())
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reply=qachain({"query": question})
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return reply
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demo = gr.Interface(fn=greet, inputs="text", outputs="text")
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if __name__ == "__main__":
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demo.launch(server_name="0.0.0.0", server_port=7860)
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