feat: Summarize messages recursively

agents-api/agents_api/activities/summarization.py

@@ -1,16 +1,20 @@
 #!/usr/bin/env python3
 
+import asyncio
 from uuid import UUID
 from typing import Callable
 from textwrap import dedent
 from temporalio import activity
 from litellm import acompletion
+from agents_api.models.entry.add_entries import add_entries_query
 from agents_api.models.entry.entries_summarization import (
     get_toplevel_entries_query,
     entries_summarization_query,
 )
 from agents_api.common.protocol.entries import Entry
-from ..env import summarization_model_name
+from agents_api.rec_sum.entities import get_entities
+from agents_api.rec_sum.summarize import summarize_messages
+from agents_api.rec_sum.trim import trim_messages
 
 
 example_previous_memory = """
@@ -148,31 +152,76 @@ async def run_prompt(
     return parser(content.strip() if content is not None else "")
 
 
+# @activity.defn
+# async def summarization(session_id: str) -> None:
+#     session_id = UUID(session_id)
+#     entries = [
+#         Entry(**row)
+#         for _, row in get_toplevel_entries_query(session_id=session_id).iterrows()
+#     ]
+
+#     assert len(entries) > 0, "no need to summarize on empty entries list"
+
+#     response = await run_prompt(
+#         dialog=entries, previous_memories=[], model=summarization_model_name
+#     )
+
+#     new_entry = Entry(
+#         session_id=session_id,
+#         source="summarizer",
+#         role="system",
+#         name="information",
+#         content=response,
+#         timestamp=entries[-1].timestamp + 0.01,
+#     )
+
+#     entries_summarization_query(
+#         session_id=session_id,
+#         new_entry=new_entry,
+#         old_entry_ids=[e.id for e in entries],
+#     )
+
+
 @activity.defn
 async def summarization(session_id: str) -> None:
     session_id = UUID(session_id)
     entries = [
-        Entry(**row)
-        for _, row in get_toplevel_entries_query(session_id=session_id).iterrows()
+        row for _, row in get_toplevel_entries_query(session_id=session_id).iterrows()
     ]
 
     assert len(entries) > 0, "no need to summarize on empty entries list"
 
-    response = await run_prompt(
-        dialog=entries, previous_memories=[], model=summarization_model_name
+    trimmed_messages, entities = await asyncio.gather(
+        trim_messages(entries),
+        get_entities(entries),
     )
-
-    new_entry = Entry(
-        session_id=session_id,
-        source="summarizer",
-        role="system",
-        name="information",
-        content=response,
-        timestamp=entries[-1].timestamp + 0.01,
+    summarized = await summarize_messages(trimmed_messages)
+
+    ts_delta = (entries[1]["timestamp"] - entries[0]["timestamp"]) / 2 
+
+    add_entries_query(
+        Entry(
+            session_id=session_id,
+            source="summarizer",
+            role="system",
+            name="entities",
+            content=entities["content"],
+            timestamp=entries[0]["timestamp"] + ts_delta,
+        )
     )
 
-    entries_summarization_query(
-        session_id=session_id,
-        new_entry=new_entry,
-        old_entry_ids=[e.id for e in entries],
-    )
+    for msg in summarized:
+        new_entry = Entry(
+            session_id=session_id,
+            source="summarizer",
+            role="system",
+            name="information",
+            content=msg["content"],
+            timestamp=entries[-1]["timestamp"] + 0.01,
+        )
+
+        entries_summarization_query(
+            session_id=session_id,
+            new_entry=new_entry,
+            old_entry_ids=[entries[idx]["entry_id"] for idx in msg["summarizes"]],
+        )

agents-api/agents_api/rec_sum/__init__.py

@@ -1,3 +0,0 @@
-from .entities import get_entities
-from .summarize import summarize_messages
-from .trim import trim_messages

agents-api/agents_api/rec_sum/data.py

@@ -5,28 +5,21 @@
 module_directory = Path(__file__).parent
 
 
-
-with open(f"{module_directory}/entities_example_chat.json", 'r') as _f:
+with open(f"{module_directory}/entities_example_chat.json", "r") as _f:
     entities_example_chat = json.load(_f)
-
 
 
-with open(f"{module_directory}/trim_example_chat.json", 'r') as _f:
+with open(f"{module_directory}/trim_example_chat.json", "r") as _f:
     trim_example_chat = json.load(_f)
-
 
 
-with open(f"{module_directory}/trim_example_result.json", 'r') as _f:
+with open(f"{module_directory}/trim_example_result.json", "r") as _f:
     trim_example_result = json.load(_f)
-
 
 
-with open(f"{module_directory}/summarize_example_chat.json", 'r') as _f:
+with open(f"{module_directory}/summarize_example_chat.json", "r") as _f:
     summarize_example_chat = json.load(_f)
-
 
 
-with open(f"{module_directory}/summarize_example_result.json", 'r') as _f:
+with open(f"{module_directory}/summarize_example_result.json", "r") as _f:
     summarize_example_result = json.load(_f)
-
-

agents-api/agents_api/rec_sum/entities.py

@@ -1,6 +1,6 @@
 import json
 
-from tenacity import retry, stop_after_attempt, wait_fixed
+from tenacity import retry, stop_after_attempt
 
 from .data import entities_example_chat
 from .generate import generate
@@ -41,38 +41,18 @@
 - See the example to get a better idea of the task."""
 
 
-make_entities_prompt = lambda session, user="a user", assistant="gpt-4-turbo", **_: [f"""\
-You are given a session history of a chat between {user or "a user"} and {assistant or "gpt-4-turbo"}. The session is formatted in the ChatML JSON format (from OpenAI).
-
-{entities_instructions}
-
-<ct:example-session>
-{json.dumps(entities_example_chat, indent=2)}
-</ct:example-session>
-
-<ct:example-plan>
-{entities_example_plan}
-</ct:example-plan>
-
-<ct:example-entities>
-{entities_example_result}
-</ct:example-entities>""",
-
-f"""\
-Begin! Write the entities as a Markdown formatted list. First write your plan inside <ct:plan></ct:plan> and then the extracted entities between <ct:entities></ct:entities>.
-
-<ct:session>
-{json.dumps(session, indent=2)}
-
-</ct:session>"""]
-
+def make_entities_prompt(session, user="a user", assistant="gpt-4-turbo", **_):
+    return [
+        f"You are given a session history of a chat between {user or 'a user'} and {assistant or 'gpt-4-turbo'}. The session is formatted in the ChatML JSON format (from OpenAI).\n\n{entities_instructions}\n\n<ct:example-session>\n{json.dumps(entities_example_chat, indent=2)}\n</ct:example-session>\n\n<ct:example-plan>\n{entities_example_plan}\n</ct:example-plan>\n\n<ct:example-entities>\n{entities_example_result}\n</ct:example-entities>",
+        f"Begin! Write the entities as a Markdown formatted list. First write your plan inside <ct:plan></ct:plan> and then the extracted entities between <ct:entities></ct:entities>.\n\n<ct:session>\n{json.dumps(session, indent=2)}\n\n</ct:session>",
+    ]
 
 
 @retry(stop=stop_after_attempt(2))
 async def get_entities(
     chat_session,
-    model="gpt-4-turbo", 
-    stop=["</ct:entities"], 
+    model="gpt-4-turbo",
+    stop=["</ct:entities"],
     temperature=0.7,
     **kwargs,
 ):
@@ -84,7 +64,7 @@ async def get_entities(
         and chat_session[0].get("name") != "entities"
     ):
         chat_session = chat_session[1:]
-        
+
     names = get_names_from_session(chat_session)
     system_prompt, user_message = make_entities_prompt(chat_session, **names)
     messages = [chatml.system(system_prompt), chatml.user(user_message)]
@@ -100,5 +80,5 @@ async def get_entities(
     result["content"] = result["content"].split("<ct:entities>")[-1].strip()
     result["role"] = "system"
     result["name"] = "entities"
-    
+
     return chatml.make(**result)

agents-api/agents_api/rec_sum/generate.py

@@ -7,16 +7,15 @@
 
 @retry(wait=wait_fixed(2), stop=stop_after_attempt(5))
 async def generate(
-    messages: list[dict], 
-    client: AsyncClient=client,
-    model: str="gpt-4-turbo",
+    messages: list[dict],
+    client: AsyncClient = client,
+    model: str = "gpt-4-turbo",
     **kwargs
 ) -> dict:
     result = await client.chat.completions.create(
         model=model, messages=messages, **kwargs
     )
-    
+
     result = result.choices[0].message.__dict__
 
     return result
-

agents-api/agents_api/rec_sum/summarize.py

@@ -1,6 +1,6 @@
 import json
 
-from tenacity import retry, stop_after_attempt, wait_fixed
+from tenacity import retry, stop_after_attempt
 
 from .data import summarize_example_chat, summarize_example_result
 from .generate import generate
@@ -24,7 +24,6 @@
 - We can safely summarize message 34's essay into just the salient points only."""
 
 
-
 summarize_instructions = """\
 Your goal is to compactify the history by coalescing redundant information in messages into their summary in order to reduce its size and save costs.
 
@@ -36,53 +35,32 @@
 - VERY IMPORTANT: Add the indices of messages that are being summarized so that those messages can then be removed from the session otherwise, there'll be no way to identify which messages to remove. See example for more details."""
 
 
-
-make_summarize_prompt = lambda session, user="a user", assistant="gpt-4-turbo", **_: [f"""\
-You are given a session history of a chat between {user or "a user"} and {assistant or "gpt-4-turbo"}. The session is formatted in the ChatML JSON format (from OpenAI).
-
-{summarize_instructions}
-
-<ct:example-session>
-{json.dumps(add_indices(summarize_example_chat), indent=2)}
-</ct:example-session>
-
-<ct:example-plan>
-{summarize_example_plan}
-</ct:example-plan>
-
-<ct:example-summarized-messages>
-{json.dumps(summarize_example_result, indent=2)}
-</ct:example-summarized-messages>""",
-
-f"""\
-Begin! Write the summarized messages as a json list just like the example above. First write your plan inside <ct:plan></ct:plan> and then your answer between <ct:summarized-messages></ct:summarized-messages>. Don't forget to add the indices of the messages being summarized alongside each summary.
-
-<ct:session>
-{json.dumps(add_indices(session), indent=2)}
-
-</ct:session>"""]
-
+def make_summarize_prompt(session, user="a user", assistant="gpt-4-turbo", **_):
+    return [
+        f"You are given a session history of a chat between {user or 'a user'} and {assistant or 'gpt-4-turbo'}. The session is formatted in the ChatML JSON format (from OpenAI).\n\n{summarize_instructions}\n\n<ct:example-session>\n{json.dumps(add_indices(summarize_example_chat), indent=2)}\n</ct:example-session>\n\n<ct:example-plan>\n{summarize_example_plan}\n</ct:example-plan>\n\n<ct:example-summarized-messages>\n{json.dumps(summarize_example_result, indent=2)}\n</ct:example-summarized-messages>",
+        f"Begin! Write the summarized messages as a json list just like the example above. First write your plan inside <ct:plan></ct:plan> and then your answer between <ct:summarized-messages></ct:summarized-messages>. Don't forget to add the indices of the messages being summarized alongside each summary.\n\n<ct:session>\n{json.dumps(add_indices(session), indent=2)}\n\n</ct:session>",
+    ]
 
 
 @retry(stop=stop_after_attempt(2))
 async def summarize_messages(
     chat_session,
-    model="gpt-4-turbo", 
-    stop=["</ct:summarized"], 
+    model="gpt-4-turbo",
+    stop=["</ct:summarized"],
     temperature=0.8,
     **kwargs,
 ):
     assert len(chat_session) > 2, "Session is too short"
 
     offset = 0
-    
+
     # Remove the system prompt if present
     if (
         chat_session[0]["role"] == "system"
         and chat_session[0].get("name") != "entities"
     ):
         chat_session = chat_session[1:]
-        
+
         # The indices are not matched up correctly
         offset = 1
 
@@ -98,7 +76,9 @@ async def summarize_messages(
     )
 
     assert "<ct:summarized-messages>" in result["content"]
-    summarized_messages = json.loads(result["content"].split("<ct:summarized-messages>")[-1].strip())
+    summarized_messages = json.loads(
+        result["content"].split("<ct:summarized-messages>")[-1].strip()
+    )
 
     assert all((msg.get("summarizes") is not None for msg in summarized_messages))
 
@@ -107,5 +87,5 @@ async def summarize_messages(
         {**msg, "summarizes": [i + offset for i in msg["summarizes"]]}
         for msg in summarized_messages
     ]
-    
+
     return summarized_messages