added app.py

app.py

@@ -0,0 +1,93 @@
+from typing import Iterable
+import streamlit as st
+import torch
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import Qdrant
+from qdrant_client import QdrantClient
+from qdrant_client.http.models import Filter, FieldCondition, MatchValue
+from config import DB_CONFIG
+
+
+@st.cache_resource
+def load_embeddings():
+    model_name = "intfloat/multilingual-e5-large"
+    model_kwargs = {"device": "cuda:0" if torch.cuda.is_available() else "cpu"}
+    encode_kwargs = {"normalize_embeddings": False}
+    embeddings = HuggingFaceEmbeddings(
+        model_name=model_name,
+        model_kwargs=model_kwargs,
+        encode_kwargs=encode_kwargs,
+    )
+    return embeddings
+
+
+EMBEDDINGS = load_embeddings()
+
+
+def make_filter_obj(options: list[dict[str]]):
+    must = []
+    for option in options:
+        must.append(
+            FieldCondition(key=option["key"], match=MatchValue(value=option["value"]))
+        )
+    filter = Filter(must=must)
+    return filter
+
+
+def get_similay(query: str, filter: Filter):
+    db_url, db_api_key, db_collection_name = DB_CONFIG
+    client = QdrantClient(url=db_url, api_key=db_api_key)
+    db = Qdrant(
+        client=client, collection_name=db_collection_name, embeddings=EMBEDDINGS
+    )
+    docs = db.similarity_search_with_score(
+        query,
+        k=20,
+        filter=filter,
+    )
+    return docs
+
+
+def main(
+    query: str,
+    repo_name: str,
+) -> Iterable[tuple[str, tuple[str, str]]]:
+    options = [{"key": "metadata.repo_name", "value": repo_name}]
+    filter = make_filter_obj(options=options)
+    docs = get_similay(query, filter)
+    for doc, score in docs:
+        text = doc.page_content
+        metadata = doc.metadata
+        # print(metadata)
+        title = metadata.get("title")
+        url = metadata.get("url")
+        id_ = metadata.get("id")
+        is_comment = metadata.get("type_") == "comment"
+        yield title, url, id_, text, score, is_comment
+
+
+with st.form("my_form"):
+    st.title("GitHub Issue Search")
+    query = st.text_input(label="query")
+    repo_name = st.radio(
+        options=["cocoa", "plone", "volto", "plone.restapi"], label="Repo name"
+    )
+
+    submitted = st.form_submit_button("Submit")
+    if submitted:
+        st.divider()
+        st.header("Search Results")
+        st.divider()
+        with st.spinner("Searching..."):
+            results = main(query, repo_name)
+            for title, url, id_, text, score, is_comment in results:
+                with st.container():
+                    if not is_comment:
+                        st.subheader(f"#{id_} - {title}")
+                    else:
+                        st.subheader(f"comment with {title}")
+                    st.write(url)
+                    st.write(text)
+                    st.write(score)
+                    # st.markdown(html, unsafe_allow_html=True)
+                    st.divider()