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import gradio as gr
from langchain.embeddings import HuggingFaceEmbeddings, HuggingFaceInstructEmbeddings, OpenAIEmbeddings
from langchain.vectorstores import Pinecone
import pinecone
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
PINECONE_KEY = os.environ.get("PINECONE_KEY", "")
PINECONE_ENV = os.environ.get("PINECONE_ENV", "us-east-1")
PINECONE_INDEX = os.environ.get("PINECONE_INDEX", '3gpp-r16-hg')
EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "hkunlp/instructor-large")
EMBEDDING_LOADER = os.environ.get("EMBEDDING_LOADER", "HuggingFaceInstructEmbeddings")
EMBEDDING_LIST = ["HuggingFaceInstructEmbeddings", "HuggingFaceEmbeddings"]
# return top-k text chunks from vector store
TOP_K_DEFAULT = 15
TOP_K_MAX = 30
SCORE_DEFAULT = 0.33
global g_db
g_db = None
def init_db(emb_name, emb_loader, db_api_key, db_env, db_index):
embeddings = eval(emb_loader)(model_name=emb_name)
pinecone.init(api_key = db_api_key,
environment = db_env)
global g_db
g_db = Pinecone.from_existing_index(index_name = db_index,
embedding = embeddings)
return str(g_db)
def get_db():
return g_db
def remove_duplicates(documents, score_min):
seen_content = set()
unique_documents = []
for (doc, score) in documents:
if (doc.page_content not in seen_content) and (score >= score_min):
seen_content.add(doc.page_content)
unique_documents.append(doc)
return unique_documents
def get_data(query, top_k, score):
if not query:
return "Please init db in configuration"
print("Use db: " + str(g_db))
docs = g_db.similarity_search_with_score(query = query,
k=top_k)
#docsearch = db.as_retriever(search_kwargs={'k':top_k})
#docs = docsearch.get_relevant_documents(query)
udocs = remove_duplicates(docs, score)
return udocs
with gr.Blocks(
title = "3GPP Database",
theme = "Base",
css = """.bigbox {
min-height:250px;
}
""") as demo:
with gr.Tab("Matching"):
with gr.Accordion("Vector similarity"):
with gr.Row():
with gr.Column():
top_k = gr.Slider(1,
TOP_K_MAX,
value=TOP_K_DEFAULT,
step=1,
label="Vector similarity top_k",
interactive=True)
with gr.Column():
score = gr.Slider(0.01,
0.99,
value=SCORE_DEFAULT,
step=0.01,
label="Vector similarity score",
interactive=True)
with gr.Row():
inp = gr.Textbox(label = "Input",
placeholder="What are you looking for?")
out = gr.Textbox(label = "Output")
btn_run = gr.Button("Run", variant="primary")
with gr.Tab("Configuration"):
with gr.Row():
loading = gr.Textbox(get_db, max_lines=1, show_label=False)
btn_init = gr.Button("Init")
with gr.Accordion("Embedding"):
with gr.Row():
with gr.Column():
emb_textbox = gr.Textbox(
label = "Embedding Model",
# show_label = False,
value = EMBEDDING_MODEL,
placeholder = "Paste Your Embedding Model Repo on HuggingFace",
lines=1,
interactive=True,
type='email')
with gr.Column():
emb_dropdown = gr.Dropdown(
EMBEDDING_LIST,
value=EMBEDDING_LOADER,
multiselect=False,
interactive=True,
label="Embedding Loader")
with gr.Accordion("Pinecone Database"):
with gr.Row():
db_api_textbox = gr.Textbox(
label = "Pinecone API Key",
# show_label = False,
value = PINECONE_KEY,
placeholder = "Paste Your Pinecone API Key (xx-xx-xx-xx-xx) and Hit ENTER",
lines=1,
interactive=True,
type='password')
with gr.Row():
db_env_textbox = gr.Textbox(
label = "Pinecone Environment",
# show_label = False,
value = PINECONE_ENV,
placeholder = "Paste Your Pinecone Environment (xx-xx-xx) and Hit ENTER",
lines=1,
interactive=True,
type='email')
db_index_textbox = gr.Textbox(
label = "Pinecone Index",
# show_label = False,
value = PINECONE_INDEX,
placeholder = "Paste Your Pinecone Index (xxxx) and Hit ENTER",
lines=1,
interactive=True,
type='email')
btn_init.click(fn=init_db, inputs=[emb_textbox, emb_dropdown, db_api_textbox, db_env_textbox, db_index_textbox], outputs=loading)
btn_run.click(fn=get_data, inputs=[inp, top_k, score], outputs=out)
if __name__ == "__main__":
demo.queue()
demo.launch(inbrowser = True)
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