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import os
import shutil
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import DirectoryLoader
from langchain_openai import OpenAIEmbeddings
from langchain.vectorstores.chroma import Chroma
from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
import gradio as gr
script_directory = os.path.dirname(os.path.abspath(__file__))
DATA_PATH = os.path.join(script_directory, "pdfs")
CHROMA_PATH = "chroma"
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
PROMPT_TEMPLATE = """
Answer the question based only on the following context:
{context}
---
Answer the question based on the above context: {question}
"""
def load_documents():
loader = DirectoryLoader(DATA_PATH, glob="*.pdf")
documents = loader.load()
return documents
def split_text(documents):
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=300,
chunk_overlap=100,
length_function=len,
add_start_index=True,
)
chunks = text_splitter.split_documents(documents)
print(f"Split {len(documents)} documents into {len(chunks)} chunks.")
return chunks
def save_to_chroma(chunks):
# Clear out the database first.
if os.path.exists(CHROMA_PATH):
shutil.rmtree(CHROMA_PATH)
embeddings = OpenAIEmbeddings()
# Create a new DB from the documents.
db = Chroma.from_documents(
chunks, embeddings, persist_directory=CHROMA_PATH
)
db.persist()
print(f"Saved {len(chunks)} chunks to {CHROMA_PATH}.")
def get_response(query_text):
# Prepare the DB.
embedding_function = OpenAIEmbeddings()
db = Chroma(persist_directory=CHROMA_PATH, embedding_function=embedding_function)
results = db.similarity_search_with_relevance_scores(query_text, k=4)
if len(results) == 0 or results[0][1] < 0.7:
print(f"Unable to find matching results.")
return
context_text = "\n\n---\n\n".join([doc.page_content for doc, _score in results])
context_text = "\n\n---\n\n".join([doc.page_content for doc, _score in results])
prompt_template = ChatPromptTemplate.from_template(PROMPT_TEMPLATE)
prompt = prompt_template.format(context=context_text, question=query_text)
model = ChatOpenAI()
response_text = model.predict(prompt)
sources = [doc.metadata.get("source", None) for doc, _score in results]
sources = list(dict.fromkeys(sources))
formatted_response = f"Response: {response_text}\nSources: {sources}"
return formatted_response
def prepare():
documents = load_documents()
chunks = split_text(documents)
save_to_chroma(chunks)
iface = gr.Interface(fn=get_response,
inputs=gr.components.Textbox(lines=7, label="Enter your text"),
outputs="text",
title="UK Insurance Law AI Tool")
prepare()
iface.launch()
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