feat: added limited retrieval-only interface

data/agentbase-v1.1.csv

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data/agentbase_mini.csv

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data/embeddings/BAAI_bge-large-en-v1.5_18d58ad4370dc14d.npz

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data/embeddings/BAAI_bge-large-en-v1.5_35225dd43945b254.npz

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data/embeddings/BAAI_bge-large-en-v1.5_63af9b74ada2385d.npz

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data/embeddings/BAAI_bge-large-en-v1.5_74e561a3a431c1aa.npz

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data/embeddings/BAAI_bge-large-en-v1.5_8c6dc4a78211c0a6.npz

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data/embeddings/BAAI_bge-large-en-v1.5_cf2365f1cf47d288.npz

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requirements.txt

@@ -3,7 +3,15 @@ pytest
 flake8
 pandas
 matplotlib
+seaborn
+scikit-learn
+scipy
+umap-learn
+hdbscan
 torch
+langchain
+langchain-community
+langchain-openai
 streamlit
 SQLAlchemy
 sentence-transformers
@@ -16,5 +24,11 @@ Scrapy
 scrapy-playwright
 PyDispatcher
 langdetect
+rouge-score
 altair<5
+pytrec-eval==0.5
+pytrec-eval-terrier==0.5.6
+mcp
+langchain-mcp-adapters
+langchain-anthropic
 standard-imghdr

interface.py → ui/interface.py

@@ -160,8 +160,8 @@ class AgentBaseUI:
 
 if __name__ == "__main__":
     BASE_DIR = Path(__file__).resolve().parent
-    agentbase_path = BASE_DIR / "data/agentbase-v1.1.csv"
-    platforms_path = BASE_DIR / "data/platforms.csv"
+    agentbase_path = BASE_DIR / "../data/agentbase-v1.1.csv"
+    platforms_path = BASE_DIR / "../data/platforms.csv"
 
     agentbaseui = AgentBaseUI(agentbase_path, platforms_path)
     agentbaseui.header_panel()

ui/retrieval_interface.py

@@ -0,0 +1,215 @@
+"""
+AgentBase Retrieval UI (limited interface).
+
+Author: Arastun Mammadli
+Date: [Current Date]
+"""
+
+from typing import List, Tuple
+from pathlib import Path
+import streamlit as st
+import pandas as pd
+import numpy as np
+import html
+
+from retrieval.models.bm25 import BM25Retriever
+from retrieval.models.sentence_bert import DenseRetriever
+from retrieval.utils import load_queries
+
+
+@st.cache_resource()
+def load_retrievers(agentbase_path: str, index_configs: List[str]) -> Tuple[dict, dict]:
+    bm25s = {}
+    bges = {}
+    for idx_config in index_configs:
+        bm25s[idx_config] = BM25Retriever(agentbase_path, index_config=idx_config)
+        bges[idx_config] = DenseRetriever("BAAI/bge-large-en-v1.5", agentbase_path, index_config=idx_config)
+    return bm25s, bges
+
+@st.cache_resource()
+def load_agentbase_data(agentbase_path: str) -> pd.DataFrame:
+    return pd.read_csv(agentbase_path)
+
+
+class AgentBaseUI:
+    """
+    AgentBase Streamlit-based UI Components.
+    """
+    def __init__(self, agentbase_path, platforms_path):
+        self.agents_df = load_agentbase_data(agentbase_path)
+        self.platforms_df = pd.read_csv(platforms_path)
+        self.bm25s, self.bges = load_retrievers(agentbase_path, index_configs=["v1", "naive"])
+
+        # selection options and defaults
+        self.retrieval_models = ["bge-large-en-v1.5", "bm25"]
+        self.selected_model = "bge-large-en-v1.5"
+        self.indexing_configs = ["v1", "naive"]
+        self.indexing_config = "v1"
+
+    def header_panel(self):
+        st.set_page_config(page_title="AgentBase", layout="wide")
+        st.title("AgentBase Retriever")
+        st.write("A Large-Scale Agent Collection for Automated Agent Recommendation.")
+
+        if "query" not in st.session_state:
+            st.session_state.query = ""
+
+        col1, col2, col3 = st.columns([4, 1, 1])
+        with col1:
+            st.text_input("", placeholder="Type to search...", key="query")
+        with col2:
+            self.selected_model = st.selectbox("", self.retrieval_models, index=0)
+        with col3:
+            self.indexing_config = st.selectbox("", self.indexing_configs, index=0)
+
+        _, col2 = st.columns([2, 1])
+        with col2:
+            with st.expander("See explanation"):
+                st.write('''
+                - **Retrieval Models**:
+                    - **BGE-Large**: a dense retrieval model.
+                    - **BM25**: a sparse retrieval model.
+                - **Indexing Configurations**:
+                    - **v1**: using all columns with priority ordering (e.g., name, description come first).
+                    - **naive**: using agent name and description only.
+                ''')
+
+    def retrieval_panel(self):
+        top_k = 5
+        if st.session_state.query:
+            self.filtered_df = self.retrieve_agents(st.session_state.query, top_k)
+        else:
+            self.filtered_df = self.agents_df.copy()
+            self.filtered_df["scores"] = 0.0
+
+        if len(self.filtered_df) > 0:
+            key_columns = [
+                "agent_name",
+                "platform_name",
+                "agent_description",
+                "agent_pricing",
+                "base_model",
+                "agent_url",
+                "scores",
+            ]
+            if (self.filtered_df["scores"] == 0).all():
+                key_columns.remove("scores")
+
+            display_df = self.filtered_df[key_columns].head(top_k)
+            csv_text = self.filtered_df.head(top_k).to_csv(index=False)
+            jsonl_text = self.filtered_df.head(top_k).to_json(orient="records", lines=True)
+
+            # --- header row with actions ---
+            col1, col2, col3 = st.columns([4, 1, 1])
+            with col1:
+                st.write(f"Showing {top_k} of {len(self.agents_df)} agents")
+            with col2:
+                copy_button(csv_text, "Copy CSV")
+            with col3:
+                copy_button(jsonl_text, "Copy JSONL")
+
+            agent_config = {
+                "agent_name": st.column_config.TextColumn(
+                    "agent_name", width="medium"
+                ),
+                "agent_url": st.column_config.LinkColumn(
+                    "agent_url", display_text="Visit →"
+                ),
+                "agent_description": st.column_config.TextColumn(
+                    "agent_description", width="large"
+                ),
+                "agent_accessibility": st.column_config.TextColumn(
+                    "agent_accessibility", width="small"
+                ),
+                "agent_pricing": st.column_config.TextColumn(
+                    "agent_pricing", width="medium"
+                ),
+                "base_model": st.column_config.TextColumn(
+                    "base_model", width="medium"
+                ),
+            }
+            st.dataframe(
+                display_df,
+                column_config=agent_config,
+                use_container_width=True,
+                hide_index=True,
+            )
+        else:
+            st.info("No agents match your search.")
+
+    def retrieve_agents(self, query, top_k=100) -> pd.DataFrame:
+        """
+        Returns a filtered dataframe with updated scores.
+        Default maximum top_k of 100
+        """
+        if self.selected_model == 'bm25':
+            res = self.bm25s[self.indexing_config].retrieve(query, top_k)
+        elif self.selected_model == 'bge-large-en-v1.5':
+            res = self.bges[self.indexing_config].retrieve(query, top_k)
+        else:
+            raise ValueError(f"Selected model must be one of {self.retrieval_models}")
+
+        self.agents_df["scores"] = 0 # reset
+        agent_ids, _ = zip(*res)
+        filtered_df = self.agents_df.loc[self.agents_df.agent_id.isin(agent_ids)]
+        for index, row in filtered_df.iterrows():
+            score = dict(res).get(row['agent_id'], 0)
+            filtered_df.at[index, 'scores'] = score
+    
+        return filtered_df.sort_values(by="scores", ascending=False)
+
+
+def copy_button(text: str, label: str):
+    safe = html.escape(text)
+    st.components.v1.html(
+        f"""
+        <button
+          onclick="
+            navigator.clipboard.writeText(`{safe}`);
+            const btn = this;
+            const original = btn.innerText;
+            btn.innerText = 'Copied';
+            btn.classList.add('copied');
+            setTimeout(() => {{
+              btn.innerText = original;
+              btn.classList.remove('copied');
+            }}, 900);
+          "
+          style="
+            width:100%;
+            padding:10px 12px;
+            border-radius:8px;
+            border:1px solid #ddd;
+            cursor:pointer;
+            font-size:14px;
+            background:#fff;
+            transition:
+              background-color 0.15s ease,
+              transform 0.08s ease,
+              box-shadow 0.08s ease;
+          "
+          onmouseover="this.style.background='#f6f7f9'"
+          onmouseout="this.style.background='#fff'"
+          onmousedown="
+            this.style.transform='scale(0.97)';
+            this.style.boxShadow='inset 0 2px 6px rgba(0,0,0,0.15)';
+          "
+          onmouseup="
+            this.style.transform='scale(1)';
+            this.style.boxShadow='none';
+          "
+        >
+          {label}
+        </button>
+        """,
+        height=52,
+    )
+
+if __name__ == "__main__":
+    BASE_DIR = Path(__file__).resolve().parent
+    agentbase_path = BASE_DIR / "../data/agentbase-v1.1.csv"
+    platforms_path = BASE_DIR / "../data/platforms.csv"
+
+    agentbaseui = AgentBaseUI(agentbase_path, platforms_path)
+    agentbaseui.header_panel()
+    agentbaseui.retrieval_panel()