fix(docs): replace stale log_stream references in published docstrings

src/splunk_ao/agent_stream.py

@@ -170,7 +170,7 @@ def create(self) -> AgentStream:
         Examples
         --------
             agent_stream = AgentStream(name="Production Logs", project_name="My AI Project").create()
-            assert log_stream.is_synced()
+            assert agent_stream.is_synced()
         """
         if not self.name:
             raise ValueError("Log stream name is not set. Cannot create log stream without a name.")
@@ -330,16 +330,16 @@ def list(
         Examples
         --------
             # List by project name
-            log_streams = AgentStream.list(project_name="My AI Project")
+            agent_streams = AgentStream.list(project_name="My AI Project")
 
             # List by project ID
-            log_streams = AgentStream.list(project_id="project-123")
+            agent_streams = AgentStream.list(project_id="project-123")
 
             # List using SPLUNK_AO_PROJECT environment variable
-            log_streams = AgentStream.list()
+            agent_streams = AgentStream.list()
 
-            # Cap the number of returned log streams
-            log_streams = AgentStream.list(project_name="My AI Project", limit=3)
+            # Cap the number of returned agent streams
+            agent_streams = AgentStream.list(project_name="My AI Project", limit=3)
 
             # Fetch the next page
             page_2 = AgentStream.list(project_name="My AI Project", starting_token=100)
@@ -371,8 +371,8 @@ def refresh(self) -> None:
 
         Examples
         --------
-            log_stream.refresh()
-            assert log_stream.is_synced()
+            agent_stream.refresh()
+            assert agent_stream.is_synced()
         """
         if self.id is None:
             raise ValueError("Log stream ID is not set. Cannot refresh a local-only log stream.")
@@ -530,14 +530,14 @@ def query(
             agent_stream = AgentStream.get(name="Production Logs", project_name="My AI Project")
 
             # Query with column-based filters and sort
-            results = log_stream.query(
+            results = agent_stream.query(
                 record_type=RecordType.SPAN,
                 filters=[
-                    log_stream.span_columns["input"].contains("largest"),
-                    log_stream.span_columns["metrics/completeness_gpt"].greater_than(0.8),
-                    log_stream.span_columns["created_at"].after("2024-01-01")
+                    agent_stream.span_columns["input"].contains("largest"),
+                    agent_stream.span_columns["metrics/completeness_gpt"].greater_than(0.8),
+                    agent_stream.span_columns["created_at"].after("2024-01-01")
                 ],
-                sort=log_stream.span_columns["created_at"].descending(),
+                sort=agent_stream.span_columns["created_at"].descending(),
                 limit=50
             )
 
@@ -626,12 +626,12 @@ def get_spans(
             agent_stream = AgentStream.get(name="Production Logs", project_name="My AI Project")
 
             # Get spans with filters and sorting
-            spans = log_stream.get_spans(
+            spans = agent_stream.get_spans(
                 filters=[
-                    log_stream.span_columns["input"].contains("world"),
-                    log_stream.span_columns["metrics/num_input_tokens"].greater_than(10)
+                    agent_stream.span_columns["input"].contains("world"),
+                    agent_stream.span_columns["metrics/num_input_tokens"].greater_than(10)
                 ],
-                sort=log_stream.span_columns["created_at"].descending(),
+                sort=agent_stream.span_columns["created_at"].descending(),
                 limit=50
             )
 
@@ -679,12 +679,12 @@ def get_traces(
             agent_stream = AgentStream.get(name="Production Logs", project_name="My AI Project")
 
             # Get traces with filters
-            traces = log_stream.get_traces(
+            traces = agent_stream.get_traces(
                 filters=[
-                    log_stream.trace_columns["input"].contains("largest"),
-                    log_stream.trace_columns["created_at"].after("2024-01-01")
+                    agent_stream.trace_columns["input"].contains("largest"),
+                    agent_stream.trace_columns["created_at"].after("2024-01-01")
                 ],
-                sort=log_stream.trace_columns["created_at"].descending(),
+                sort=agent_stream.trace_columns["created_at"].descending(),
                 limit=50
             )
 
@@ -728,12 +728,12 @@ def get_sessions(
             agent_stream = AgentStream.get(name="Production Logs", project_name="My AI Project")
 
             # Get sessions with filters
-            sessions = log_stream.get_sessions(
+            sessions = agent_stream.get_sessions(
                 filters=[
-                    log_stream.session_columns["model"].equals("gpt-4o-mini"),
-                    log_stream.session_columns["metrics/num_traces"].greater_than(5)
+                    agent_stream.session_columns["model"].equals("gpt-4o-mini"),
+                    agent_stream.session_columns["metrics/num_traces"].greater_than(5)
                 ],
-                sort=log_stream.session_columns["created_at"].descending(),
+                sort=agent_stream.session_columns["created_at"].descending(),
                 limit=50
             )
 
@@ -784,13 +784,13 @@ def export_records(
             agent_stream = AgentStream.get(name="Production Logs", project_name="My AI Project")
 
             # Export records with filters
-            for record in log_stream.export_records(
+            for record in agent_stream.export_records(
                 record_type=RecordType.SPAN,
                 filters=[
-                    log_stream.span_columns["model"].one_of(["gpt-4", "gpt-3.5-turbo", "gpt-4o-mini"]),
-                    log_stream.span_columns["metrics/num_input_tokens"].greater_than(1)
+                    agent_stream.span_columns["model"].one_of(["gpt-4", "gpt-3.5-turbo", "gpt-4o-mini"]),
+                    agent_stream.span_columns["metrics/num_input_tokens"].greater_than(1)
                 ],
-                sort=log_stream.span_columns["created_at"].descending()
+                sort=agent_stream.span_columns["created_at"].descending()
             ):
                 print(record)
         """
@@ -836,7 +836,7 @@ def context(self) -> Any:
                 project_name="My AI Project"
             )
 
-            with log_stream.context():
+            with agent_stream.context():
                 # Your logging code here
                 response = openai_client.chat.completions.create(...)
         """
@@ -879,13 +879,13 @@ def span_columns(self) -> ColumnCollection:
         Examples
         --------
             agent_stream = AgentStream.get(name="Production Logs", project_name="My AI Project")
-            columns = log_stream.span_columns
+            columns = agent_stream.span_columns
 
             # Access a specific column
             input_column = columns["input"]
 
             # Filter using columns
-            spans = log_stream.get_spans(
+            spans = agent_stream.get_spans(
                 filters=[columns["input"].contains("world")],
                 sort=columns["created_at"].descending()
             )
@@ -912,13 +912,13 @@ def session_columns(self) -> ColumnCollection:
         Examples
         --------
             agent_stream = AgentStream.get(name="Production Logs", project_name="My AI Project")
-            columns = log_stream.session_columns
+            columns = agent_stream.session_columns
 
             # Access a specific column
             model_column = columns["model"]
 
             # Filter using columns
-            sessions = log_stream.get_sessions(
+            sessions = agent_stream.get_sessions(
                 filters=[columns["model"].equals("gpt-4o-mini")],
                 sort=columns["created_at"].descending()
             )
@@ -946,13 +946,13 @@ def trace_columns(self) -> ColumnCollection:
         Examples
         --------
             agent_stream = AgentStream.get(name="Production Logs", project_name="My AI Project")
-            columns = log_stream.trace_columns
+            columns = agent_stream.trace_columns
 
             # Access a specific column
             input_column = columns["input"]
 
             # Filter using columns
-            traces = log_stream.get_traces(
+            traces = agent_stream.get_traces(
                 filters=[columns["input"].contains("largest")],
                 sort=columns["created_at"].descending()
             )

src/splunk_ao/agent_streams.py

@@ -60,11 +60,11 @@ class AgentStream(LogStreamResponse):
     from splunk_ao.agent_streams import get_agent_stream
     agent_stream = get_agent_stream(name="Production Logs", project_name="My AI Project")
 
-    # List all log streams in a project
+    # List all agent streams in a project
     from splunk_ao.agent_streams import list_agent_streams
-    log_streams = list_agent_streams(project_name="My AI Project")
-    for stream in log_streams:
-        logger.info(f"Log Stream: {stream.name} (ID: {stream.id})")
+    agent_streams = list_agent_streams(project_name="My AI Project")
+    for stream in agent_streams:
+        logger.info(f"Agent Stream: {stream.name} (ID: {stream.id})")
 
     # Use a log stream with the context manager
     from splunk_ao.openai import openai

src/splunk_ao/decorator.py

@@ -212,8 +212,8 @@ def __call__(
         ----------
         project
             The project name to use for this context
-        agent_stream: The log stream name to use for this context
-            The log stream name to use for this context
+        agent_stream
+            The agent stream name to use for this context
         experiment_id
             The experiment ID to use for this context
         mode
@@ -1209,18 +1209,18 @@ def get_logger_instance(
             Optional project name to use
         project_id
             Optional project ID to use
-        log_stream
-            Optional log stream name to use
-        log_stream_id
-            Optional log stream ID to use
+        agent_stream
+            Optional agent stream name to use
+        agent_stream_id
+            Optional agent stream ID to use
         experiment_id
             Optional experiment ID to use
         mode
             Optional logger mode to use
 
         Returns
         -------
-        SplunkAOLogger instance configured with the specified project and log stream
+        SplunkAOLogger instance configured with the specified project and agent stream
         """
         kwargs = {
             "project": project if project is not None else (None if project_id is not None else _project_context.get()),
@@ -1309,16 +1309,16 @@ def flush(
         on_error: Callable[[Exception], None] | None = None,
     ) -> None:
         """
-        Upload all captured traces under a project and log stream context to Splunk AO.
+        Upload all captured traces under a project and agent stream context to Splunk AO.
 
-        If no project or log stream is provided, then the currently initialized context is used.
+        If no project or agent stream is provided, then the currently initialized context is used.
 
         Parameters
         ----------
         project
             The project name. Defaults to None.
-        log_stream
-            The log stream name. Defaults to None.
+        agent_stream
+            The agent stream name. Defaults to None.
         experiment_id
             The experiment ID. Defaults to None.
         mode
@@ -1401,18 +1401,18 @@ def init(
         mode: str | None = None,
     ) -> None:
         """
-        Initialize the context with a project and log stream. Optionally, it can also be used
+        Initialize the context with a project and agent stream. Optionally, it can also be used
         to start a trace.
 
         This method resets the existing active context with a new context with
-        the specified project and log stream.
+        the specified project and agent stream.
 
         Parameters
         ----------
         project
             The project name. Defaults to None.
-        log_stream
-            The log stream name. Defaults to None.
+        agent_stream
+            The agent stream name. Defaults to None.
         experiment_id
             The experiment id. Defaults to None.
         local_metrics

src/splunk_ao/utils/singleton.py

@@ -18,7 +18,7 @@ class SplunkAOLoggerSingleton:
 
     This class ensures that only one instance exists across the application and
     provides a thread-safe way to retrieve or create SplunkAOLogger clients based on
-    the given 'project' and 'log_stream' parameters. If the parameters are not provided,
+    the given 'project' and 'agent_stream' parameters. If the parameters are not provided,
     the class attempts to read the values from the environment variables
     SPLUNK_AO_PROJECT and SPLUNK_AO_AGENT_STREAM. The loggers are stored in a dictionary
     using a tuple (project, agent_stream) as the key.
@@ -165,16 +165,16 @@ def get(
         """
         Retrieve an existing SplunkAOLogger or create a new one if it does not exist.
 
-        This method first computes the key from the project and log_stream parameters,
+        This method first computes the key from the project and agent_stream parameters,
         checks if a logger exists in the cache, and if not, creates a new SplunkAOLogger.
         The creation and caching are done in a thread-safe manner.
 
         Parameters
         ----------
         project (Optional[str], optional)
             The project name. Defaults to None.
-        log_stream (Optional[str], optional)
-            The log stream name. Defaults to None.
+        agent_stream (Optional[str], optional)
+            The agent stream name. Defaults to None.
         experiment_id (Optional[str], optional)
             The experiment ID. Defaults to None.
         local_metrics (Optional[list[LocalScorerConfig]], optional)
@@ -252,10 +252,10 @@ def reset(
             The project name. Defaults to None.
         project_id (Optional[str], optional)
             The project ID. Defaults to None.
-        log_stream (Optional[str], optional)
-            The log stream name. Defaults to None.
-        log_stream_id (Optional[str], optional)
-            The log stream ID. Defaults to None.
+        agent_stream (Optional[str], optional)
+            The agent stream name. Defaults to None.
+        agent_stream_id (Optional[str], optional)
+            The agent stream ID. Defaults to None.
         experiment_id (Optional[str], optional)
             The experiment ID. Defaults to None.
         mode (Optional[str], optional)
@@ -297,7 +297,7 @@ def flush(
         """
         Flush (upload and clear) a SplunkAOLogger instance.
 
-        If both project and log_stream are None, then all cached loggers are flushed
+        If both project and agent_stream are None, then all cached loggers are flushed
         and cleared. Otherwise, only the specific logger corresponding to the provided
         key (project, agent_stream) is flushed and removed.
 
@@ -307,10 +307,10 @@ def flush(
             The project name. Defaults to None.
         project_id (Optional[str], optional)
             The project ID. Defaults to None.
-        log_stream (Optional[str], optional)
-            The log stream name. Defaults to None.
-        log_stream_id (Optional[str], optional)
-            The log stream ID. Defaults to None.
+        agent_stream (Optional[str], optional)
+            The agent stream name. Defaults to None.
+        agent_stream_id (Optional[str], optional)
+            The agent stream ID. Defaults to None.
         experiment_id (Optional[str], optional)
             The experiment ID. Defaults to None.
         mode (Optional[str], optional)