Update trace_agent_collaboratif.py

trace_agent_collaboratif.py

@@ -1,104 +1,104 @@
-import os
-import sys
-import importlib
-from collections import defaultdict
-
-from concurrent.futures import Future, ThreadPoolExecutor
-
-from datetime import datetime, timedelta
-import pandas as pd
-from langsmith import Client
-from tqdm.auto import tqdm
-
-import chainlit as cl
-
-async def get_trace(apiKey,task_list):
-    try:
-        client = Client(api_key=apiKey)
-        project_name = "agent-collaboratif-avid"
-        num_days = 30
-
-        # List all tool runs
-        tool_runs = client.list_runs(
-            project_name=project_name,
-            start_time=datetime.now() - timedelta(days=num_days),
-            is_root=True,
-            # We don't need to fetch inputs, outputs, and other values that # may increase the query time
-            select=["inputs","trace_id"],
-        )
-
-        data = []
-        futures: list[Future] = []
-        trace_cursor = 0
-        trace_batch_size = 20
-
-        tool_runs_by_parent = defaultdict(lambda: defaultdict(set))
-        # Do not exceed rate limit
-        with ThreadPoolExecutor(max_workers=2) as executor:
-            # Group tool runs by parent run ID
-            task2 = cl.Task(title="Grouper les outils invoqués dans une trace et les organiser par parent run ID")
-            await task_list.add_task(task2)
-            for run in tqdm(tool_runs):
-                # Collect all tools invoked within a given trace
-                tool_runs_by_parent[run.trace_id]["tools_involved"].add(run.name)
-                # maybe send a batch of parent run IDs to the server
-                # this lets us query for the root runs in batches
-                # while still processing the tool runs
-                if len(tool_runs_by_parent) % trace_batch_size == 0:
-                    if this_batch := list(tool_runs_by_parent.keys())[
-                        trace_cursor : trace_cursor + trace_batch_size
-                    ]:
-                        trace_cursor += trace_batch_size
-                        futures.append(
-                            executor.submit(
-                                client.list_runs,
-                                project_name=project_name,
-                                run_ids=this_batch,
-                                select=["inputs","trace_id"],
-                            )
-                        )
-            await task_list.send()
-            if this_batch := list(tool_runs_by_parent.keys())[trace_cursor:]:
-                futures.append(
-                    executor.submit(
-                        client.list_runs,
-                        project_name=project_name,
-                        run_ids=this_batch,
-                        select=["inputs","trace_id"],
-                    )
-                )
-        task2.status = cl.TaskStatus.DONE
-        await task_list.send()
-        task3 = cl.Task(title="Rechercher les données d'actions des utilisateurs de l'agent collabroatif AVID et les organiser par parent run ID dans un DataFrame")
-        await task_list.add_task(task3)
-        for future in tqdm(futures):
-            root_runs = future.result()
-            for root_run in root_runs:
-                root_data = tool_runs_by_parent[root_run.id]
-                data.append(
-                    {
-                        "inputs": root_run.inputs,
-                        "start_time": root_run.start_time,
-                        "end_time": root_run.end_time,
-                    }
-                )
-
-        # (Optional): Convert to a pandas DataFrame
-        task3.status = cl.TaskStatus.DONE
-        await task_list.send()
-        
-        df_inputs = pd.DataFrame(data)
-        df_inputs['query'] = df_inputs.apply(lambda x: x.get('inputs', {}).get('query'), axis=1)
-        df_inputs['latency'] = df_inputs['end_time'] - df_inputs['start_time']
-        df_inputs['latency'] = df_inputs['latency'].apply(lambda x: x.total_seconds())
-        df_inputs=df_inputs[["query","latency","start_time"]].copy()
-        task4 = cl.Task(title="Conversion des données d'actions des utilisateurs de l'agent collabroatif AVID et les afficher au format texte")
-        await task_list.add_task(task4)
-        
-        list_inputs = df_inputs.head(20).values.tolist()
-        str_inputs="".join(['* Requête : ' + str(item[0]) + '\nDate : ' + str(item[2]) + '\nDurée de la requête : ' + str(item[1]) + '\n\n' for item in list_inputs])
-        task4.status = cl.TaskStatus.DONE
-        await task_list.send()
-        return str_inputs
-    except Exception as e:
+import os
+import sys
+import importlib
+from collections import defaultdict
+
+from concurrent.futures import Future, ThreadPoolExecutor
+
+from datetime import datetime, timedelta
+import pandas as pd
+from langsmith import Client
+from tqdm.auto import tqdm
+
+import chainlit as cl
+
+async def get_trace(apiKey,task_list):
+    try:
+        client = Client(api_key=apiKey)
+        project_name = "agent-collaboratif-avid"
+        num_days = 30
+
+        # List all tool runs
+        tool_runs = client.list_runs(
+            project_name=project_name,
+            start_time=datetime.now() - timedelta(days=num_days),
+            is_root=True,
+            # We don't need to fetch inputs, outputs, and other values that # may increase the query time
+            select=["inputs","trace_id"],
+        )
+
+        data = []
+        futures: list[Future] = []
+        trace_cursor = 0
+        trace_batch_size = 20
+
+        tool_runs_by_parent = defaultdict(lambda: defaultdict(set))
+        # Do not exceed rate limit
+        with ThreadPoolExecutor(max_workers=2) as executor:
+            # Group tool runs by parent run ID
+            task2 = cl.Task(title="Grouper les outils invoqués dans une trace et les organiser par parent run ID")
+            await task_list.add_task(task2)
+            for run in tqdm(tool_runs):
+                # Collect all tools invoked within a given trace
+                tool_runs_by_parent[run.trace_id]["tools_involved"].add(run.name)
+                # maybe send a batch of parent run IDs to the server
+                # this lets us query for the root runs in batches
+                # while still processing the tool runs
+                if len(tool_runs_by_parent) % trace_batch_size == 0:
+                    if this_batch := list(tool_runs_by_parent.keys())[
+                        trace_cursor : trace_cursor + trace_batch_size
+                    ]:
+                        trace_cursor += trace_batch_size
+                        futures.append(
+                            executor.submit(
+                                client.list_runs,
+                                project_name=project_name,
+                                run_ids=this_batch,
+                                select=["inputs","trace_id"],
+                            )
+                        )
+            await task_list.send()
+            if this_batch := list(tool_runs_by_parent.keys())[trace_cursor:]:
+                futures.append(
+                    executor.submit(
+                        client.list_runs,
+                        project_name=project_name,
+                        run_ids=this_batch,
+                        select=["inputs","trace_id"],
+                    )
+                )
+        task2.status = cl.TaskStatus.DONE
+        await task_list.send()
+        task3 = cl.Task(title="Rechercher les données d'actions des utilisateurs de l'agent collabroatif AVID et les organiser par parent run ID dans un DataFrame")
+        await task_list.add_task(task3)
+        for future in tqdm(futures):
+            root_runs = future.result()
+            for root_run in root_runs:
+                root_data = tool_runs_by_parent[root_run.id]
+                data.append(
+                    {
+                        "inputs": root_run.inputs,
+                        "start_time": root_run.start_time,
+                        "end_time": root_run.end_time,
+                    }
+                )
+
+        # (Optional): Convert to a pandas DataFrame
+        task3.status = cl.TaskStatus.DONE
+        await task_list.send()
+        
+        df_inputs = pd.DataFrame(data)
+        df_inputs['query'] = df_inputs.apply(lambda x: x.get('inputs', {}).get('query'), axis=1)
+        df_inputs['latency'] = df_inputs['end_time'] - df_inputs['start_time']
+        df_inputs['latency'] = df_inputs['latency'].apply(lambda x: x.total_seconds())
+        df_inputs=df_inputs[["query","latency","start_time"]].copy()
+        task4 = cl.Task(title="Conversion des données d'actions des utilisateurs de l'agent collabroatif AVID et les afficher au format texte")
+        await task_list.add_task(task4)
+        
+        list_inputs = df_inputs.head(20).values.tolist()
+        str_inputs="".join(['* Requête : ' + str(item[0]) + '\nDate : ' + str(item[2]) + '\nDurée de la requête : ' + str(item[1]) + '\n\n' for item in list_inputs])
+        task4.status = cl.TaskStatus.DONE
+        await task_list.send()
+        return str_inputs
+    except Exception as e:
         return f"Aucune connexion à LangSmith"