Merge branch 'dev' into dataset-permissions

cognee/api/v1/delete/routers/get_delete_router.py

@@ -58,8 +58,8 @@ def get_delete_router() -> APIRouter:
                         )
                         results.append(result)
                 else:
-                    # Handle uploaded file
+                    # Handle uploaded file by accessing its file attribute
-                    result = await cognee_delete(file, dataset_name=dataset_name, mode=mode)
+                    result = await cognee_delete(file.file, dataset_name=dataset_name, mode=mode)
                     results.append(result)
 
             if len(results) == 1:

cognee/eval_framework/modal_eval_dashboard.py

@@ -0,0 +1,94 @@
+import os
+import json
+import pandas as pd
+import subprocess
+import modal
+import streamlit as st
+
+# ----------------------------------------------------------------------------
+# Volume and Image Setup
+# ----------------------------------------------------------------------------
+metrics_volume = modal.Volume.from_name("evaluation_dashboard_results", create_if_missing=True)
+
+image = (
+    modal.Image.debian_slim(python_version="3.11")
+    .pip_install("streamlit", "pandas", "plotly")
+    .add_local_file(__file__, "/root/serve_dashboard.py")
+)
+
+# ----------------------------------------------------------------------------
+# Define and Deploy the App
+# ----------------------------------------------------------------------------
+app = modal.App(
+    name="metrics-dashboard",
+    image=image,
+    volumes={"/data": metrics_volume},
+)
+
+
+@app.function()
+@modal.web_server(port=8000)
+def run():
+    """
+    Launch Streamlit server on port 8000 inside the container.
+    """
+    cmd = (
+        "streamlit run /root/serve_dashboard.py "
+        "--server.port 8000 "
+        "--server.enableCORS=false "
+        "--server.enableXsrfProtection=false"
+    )
+    subprocess.Popen(cmd, shell=True)
+
+
+# ----------------------------------------------------------------------------
+# Streamlit Dashboard Application Logic
+# ----------------------------------------------------------------------------
+def main():
+    st.set_page_config(page_title="Metrics Dashboard", layout="wide")
+    st.title("📊 Cognee Evaluations Dashboard")
+
+    data_path = "/data"
+    records = []
+
+    for filename in sorted(os.listdir(data_path)):
+        if not filename.endswith(".json"):
+            continue
+        base = filename.rsplit(".", 1)[0]
+        parts = base.split("_")
+        benchmark = parts[1] if len(parts) >= 3 else ""
+
+        full_path = os.path.join(data_path, filename)
+        with open(full_path, "r") as f:
+            items = json.load(f)
+        num_q = len(items)
+        total_em = sum(q["metrics"]["EM"]["score"] for q in items)
+        total_f1 = sum(q["metrics"]["f1"]["score"] for q in items)
+        total_corr = sum(q["metrics"]["correctness"]["score"] for q in items)
+        records.append(
+            {
+                "file": parts[0].upper() + "_____" + parts[2],
+                "benchmark": benchmark,
+                "questions": num_q,
+                "avg_EM": round(total_em / num_q, 4),
+                "avg_F1": round(total_f1 / num_q, 4),
+                "avg_correctness": round(total_corr / num_q, 4),
+            }
+        )
+
+    df = pd.DataFrame(records)
+    if df.empty:
+        st.warning("No JSON files found in the volume.")
+        return
+
+    st.subheader("Results by benchmark")
+    for bm, group in df.groupby("benchmark"):
+        st.markdown(f"### {bm}")
+        st.dataframe(
+            group[["file", "questions", "avg_EM", "avg_F1", "avg_correctness"]],
+            use_container_width=True,
+        )
+
+
+if __name__ == "__main__":
+    main()

cognee/infrastructure/llm/generic_llm_api/adapter.py

@@ -40,8 +40,7 @@ class GenericAPIAdapter(LLMInterface):
             messages=[
                 {
                     "role": "user",
-                    "content": f"""Use the given format to
+                    "content": f"""{text_input}""",
-                extract information from the following input: {text_input}. """,
                 },
                 {
                     "role": "system",

cognee/infrastructure/llm/ollama/adapter.py

@@ -39,7 +39,7 @@ class OllamaAPIAdapter(LLMInterface):
             messages=[
                 {
                     "role": "user",
-                    "content": f"Use the given format to extract information from the following input: {text_input}",
+                    "content": f"{text_input}",
                 },
                 {
                     "role": "system",

cognee/infrastructure/llm/openai/adapter.py

@@ -63,8 +63,7 @@ class OpenAIAdapter(LLMInterface):
             messages=[
                 {
                     "role": "user",
-                    "content": f"""Use the given format to
+                    "content": f"""{text_input}""",
-                extract information from the following input: {text_input}. """,
                 },
                 {
                     "role": "system",
@@ -91,8 +90,7 @@ class OpenAIAdapter(LLMInterface):
             messages=[
                 {
                     "role": "user",
-                    "content": f"""Use the given format to
+                    "content": f"""{text_input}""",
-                extract information from the following input: {text_input}. """,
                 },
                 {
                     "role": "system",