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dashboard ukpl data with hybrid search (#28)
* hybrid search works * fix text area * fix linter * fix nbdev
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,40 +1,60 @@ | ||
| from typing import Any | ||
| import streamlit as st | ||
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| from juddges.data.datasets import get_mongo_collection | ||
| import streamlit as st | ||
| from pymongo.collection import Collection | ||
| from sentence_transformers import SentenceTransformer | ||
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| from juddges.data.database import get_mongo_collection | ||
| from juddges.retrieval.mongo_hybrid_search import run_hybrid_search | ||
| from juddges.settings import TEXT_EMBEDDING_MODEL | ||
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| TITLE = "Search for Judgements" | ||
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| st.set_page_config(page_title=TITLE, page_icon="⚖️", layout="wide") | ||
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| st.title(TITLE) | ||
| st.header( | ||
| "Search is based on hybrid search using text and vector search with the same priority for both." | ||
| ) | ||
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| judgement_country = st.sidebar.selectbox("Select judgement country", ["pl", "uk"]) | ||
| judgement_collection_name = f"{judgement_country}-court" | ||
| st.sidebar.info(f"Selected country: {judgement_collection_name}") | ||
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| @st.cache_resource | ||
| def get_judgements_collection() -> Collection: | ||
| return get_mongo_collection("judgements") | ||
| def get_judgements_collection(collection_name: str = "pl-court") -> Collection: | ||
| return get_mongo_collection(collection_name=collection_name) | ||
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| judgements_collection = get_judgements_collection() | ||
| @st.cache_resource | ||
| def get_embedding_model() -> Any: | ||
| return SentenceTransformer(TEXT_EMBEDDING_MODEL) | ||
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| def search_data(query: str, max_judgements: int = 5) -> list[dict[str, Any]]: | ||
| items = list(judgements_collection.find({"$text": {"$search": query}}).limit(max_judgements)) | ||
| return items | ||
| judgements_collection = get_judgements_collection(judgement_collection_name) | ||
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| model = get_embedding_model() | ||
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| with st.form(key="search_form"): | ||
| text = st.text_area("What you are looking for in the judgements?") | ||
| query = st.text_area("What you are looking for in the judgements?") | ||
| max_judgements = st.slider("Max judgements to show", min_value=1, max_value=20, value=5) | ||
| submit_button = st.form_submit_button(label="Search") | ||
|
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| if submit_button: | ||
| with st.spinner("Searching..."): | ||
| items = search_data(text, max_judgements) | ||
| items = run_hybrid_search( | ||
| collection=judgements_collection, | ||
| collection_name=judgement_collection_name, | ||
| embedding=model.encode(query).tolist(), | ||
| query=query, | ||
| limit=max_judgements, | ||
| ) | ||
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| st.header("Judgements - Results") | ||
| for item in items: | ||
| st.header(item["signature"]) | ||
| st.subheader(item["publicationDate"]) | ||
| st.write(item["text"]) | ||
| st.info(f"Department: {item['department_name']}") | ||
| st.info(f"Score: {item['score']}") | ||
| st.subheader(item["excerpt"]) | ||
| st.text_area(label="Judgement text", value=item["text"], height=200) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,110 @@ | ||
| def run_hybrid_search( | ||
| collection, | ||
| collection_name: str, | ||
| embedding, | ||
| query: str, | ||
| limit: int = 10, | ||
| vector_priority: float = 1, | ||
| text_priority: float = 1, | ||
| ): | ||
| num_candidates = limit * 10 | ||
|
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| vector_search = { | ||
| "$vectorSearch": { | ||
| "index": "vector_index", | ||
| "path": "embedding", | ||
| "queryVector": embedding, | ||
| "numCandidates": num_candidates, | ||
| "limit": limit, | ||
| } | ||
| } | ||
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| make_array = {"$group": {"_id": None, "docs": {"$push": "$$ROOT"}}} | ||
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| add_rank = {"$unwind": {"path": "$docs", "includeArrayIndex": "rank"}} | ||
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| def make_compute_score_doc(priority, score_field_name): | ||
| return { | ||
| "$addFields": {score_field_name: {"$divide": [1.0, {"$add": ["$rank", priority, 1]}]}} | ||
| } | ||
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| def make_projection_doc(score_field_name): | ||
| return { | ||
| "$project": { | ||
| score_field_name: 1, | ||
| "_id": "$docs._id", | ||
| "excerpt": "$docs.excerpt", | ||
| "text": "$docs.text", | ||
| "department_name": "$docs.department_name", | ||
| "signature": "$docs.signature", | ||
| } | ||
| } | ||
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| text_search = { | ||
| "$search": { | ||
| "index": "text_index", | ||
| "text": {"query": query, "path": "text"}, | ||
| } | ||
| } | ||
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| limit_results = {"$limit": limit} | ||
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| combine_search_results = { | ||
| "$group": { | ||
| "_id": "$_id", | ||
| "vs_score": {"$max": "$vs_score"}, | ||
| "ts_score": {"$max": "$ts_score"}, | ||
| "excerpt": {"$first": "$excerpt"}, | ||
| "text": {"$first": "$text"}, | ||
| "department_name": {"$first": "$department_name"}, | ||
| "signature": {"$first": "$signature"}, | ||
| } | ||
| } | ||
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| project_combined_results = { | ||
| "$project": { | ||
| "_id": 1, | ||
| "excerpt": 1, | ||
| "text": 1, | ||
| "department_name": 1, | ||
| "signature": 1, | ||
| "score": { | ||
| "$let": { | ||
| "vars": { | ||
| "vs_score": {"$ifNull": ["$vs_score", 0]}, | ||
| "ts_score": {"$ifNull": ["$ts_score", 0]}, | ||
| }, | ||
| "in": {"$add": ["$$vs_score", "$$ts_score"]}, | ||
| } | ||
| }, | ||
| } | ||
| } | ||
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| sort_results = {"$sort": {"score": -1}} | ||
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| pipeline = [ | ||
| vector_search, | ||
| make_array, | ||
| add_rank, | ||
| make_compute_score_doc(vector_priority, "vs_score"), | ||
| make_projection_doc("vs_score"), | ||
| { | ||
| "$unionWith": { | ||
| "coll": collection_name, | ||
| "pipeline": [ | ||
| text_search, | ||
| limit_results, | ||
| make_array, | ||
| add_rank, | ||
| make_compute_score_doc(text_priority, "ts_score"), | ||
| make_projection_doc("ts_score"), | ||
| ], | ||
| } | ||
| }, | ||
| combine_search_results, | ||
| project_combined_results, | ||
| sort_results, | ||
| limit_results, | ||
| ] | ||
|
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| return collection.aggregate(pipeline) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1 @@ | ||
| /.quarto/ |
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