Harrison/virtual time (#4658)

Co-authored-by: ifsheldon <39153080+ifsheldon@users.noreply.github.com>
Co-authored-by: maple.liang <maple.liang@gempoll.com>

docs/modules/indexes/retrievers/examples/time_weighted_vectorstore.ipynb

@@ -70,7 +70,7 @@
     {
      "data": {
       "text/plain": [
-       "['5c9f7c06-c9eb-45f2-aea5-efce5fb9f2bd']"
+       "['d7f85756-2371-4bdf-9140-052780a0f9b3']"
       ]
      },
      "execution_count": 3,
@@ -93,7 +93,7 @@
     {
      "data": {
       "text/plain": [
-       "[Document(page_content='hello world', metadata={'last_accessed_at': datetime.datetime(2023, 4, 16, 22, 9, 1, 966261), 'created_at': datetime.datetime(2023, 4, 16, 22, 9, 0, 374683), 'buffer_idx': 0})]"
+       "[Document(page_content='hello world', metadata={'last_accessed_at': datetime.datetime(2023, 5, 13, 21, 0, 27, 678341), 'created_at': datetime.datetime(2023, 5, 13, 21, 0, 27, 279596), 'buffer_idx': 0})]"
       ]
      },
      "execution_count": 4,
@@ -177,10 +177,51 @@
     "retriever.get_relevant_documents(\"hello world\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "32e0131e",
+   "metadata": {},
+   "source": [
+    "## Virtual Time\n",
+    "\n",
+    "Using some utils in LangChain, you can mock out the time component"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "da080d40",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.utils import mock_now\n",
+    "import datetime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7c7deff1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Document(page_content='hello world', metadata={'last_accessed_at': MockDateTime(2011, 2, 3, 10, 11), 'created_at': datetime.datetime(2023, 5, 13, 21, 0, 27, 279596), 'buffer_idx': 0})]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Notice the last access time is that date time\n",
+    "with mock_now(datetime.datetime(2011, 2, 3, 10, 11)):\n",
+    "    print(retriever.get_relevant_documents(\"hello world\"))"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bf6d8c90",
+   "id": "c78d367d",
    "metadata": {},
    "outputs": [],
    "source": []

langchain/experimental/generative_agents/generative_agent.py

@@ -88,7 +88,9 @@ Relevant context:
         q2 = f"{entity_name} is {entity_action}"
         return self.chain(prompt=prompt).run(q1=q1, queries=[q1, q2]).strip()
 
-    def _generate_reaction(self, observation: str, suffix: str) -> str:
+    def _generate_reaction(
+        self, observation: str, suffix: str, now: Optional[datetime] = None
+    ) -> str:
         """React to a given observation or dialogue act."""
         prompt = PromptTemplate.from_template(
             "{agent_summary_description}"
@@ -101,9 +103,13 @@ Relevant context:
             + "\n\n"
             + suffix
         )
-        agent_summary_description = self.get_summary()
+        agent_summary_description = self.get_summary(now=now)
         relevant_memories_str = self.summarize_related_memories(observation)
-        current_time_str = datetime.now().strftime("%B %d, %Y, %I:%M %p")
+        current_time_str = (
+            datetime.now().strftime("%B %d, %Y, %I:%M %p")
+            if now is None
+            else now.strftime("%B %d, %Y, %I:%M %p")
+        )
         kwargs: Dict[str, Any] = dict(
             agent_summary_description=agent_summary_description,
             current_time=current_time_str,
@@ -121,7 +127,9 @@ Relevant context:
     def _clean_response(self, text: str) -> str:
         return re.sub(f"^{self.name} ", "", text.strip()).strip()
 
-    def generate_reaction(self, observation: str) -> Tuple[bool, str]:
+    def generate_reaction(
+        self, observation: str, now: Optional[datetime] = None
+    ) -> Tuple[bool, str]:
         """React to a given observation."""
         call_to_action_template = (
             "Should {agent_name} react to the observation, and if so,"
@@ -130,14 +138,17 @@ Relevant context:
             + "\notherwise, write:\nREACT: {agent_name}'s reaction (if anything)."
             + "\nEither do nothing, react, or say something but not both.\n\n"
         )
-        full_result = self._generate_reaction(observation, call_to_action_template)
+        full_result = self._generate_reaction(
+            observation, call_to_action_template, now=now
+        )
         result = full_result.strip().split("\n")[0]
         # AAA
         self.memory.save_context(
             {},
             {
                 self.memory.add_memory_key: f"{self.name} observed "
-                f"{observation} and reacted by {result}"
+                f"{observation} and reacted by {result}",
+                self.memory.now_key: now,
             },
         )
         if "REACT:" in result:
@@ -149,14 +160,18 @@ Relevant context:
         else:
             return False, result
 
-    def generate_dialogue_response(self, observation: str) -> Tuple[bool, str]:
+    def generate_dialogue_response(
+        self, observation: str, now: Optional[datetime] = None
+    ) -> Tuple[bool, str]:
         """React to a given observation."""
         call_to_action_template = (
             "What would {agent_name} say? To end the conversation, write:"
             ' GOODBYE: "what to say". Otherwise to continue the conversation,'
             ' write: SAY: "what to say next"\n\n'
         )
-        full_result = self._generate_reaction(observation, call_to_action_template)
+        full_result = self._generate_reaction(
+            observation, call_to_action_template, now=now
+        )
         result = full_result.strip().split("\n")[0]
         if "GOODBYE:" in result:
             farewell = self._clean_response(result.split("GOODBYE:")[-1])
@@ -164,7 +179,8 @@ Relevant context:
                 {},
                 {
                     self.memory.add_memory_key: f"{self.name} observed "
-                    f"{observation} and said {farewell}"
+                    f"{observation} and said {farewell}",
+                    self.memory.now_key: now,
                 },
             )
             return False, f"{self.name} said {farewell}"
@@ -174,7 +190,8 @@ Relevant context:
                 {},
                 {
                     self.memory.add_memory_key: f"{self.name} observed "
-                    f"{observation} and said {response_text}"
+                    f"{observation} and said {response_text}",
+                    self.memory.now_key: now,
                 },
             )
             return True, f"{self.name} said {response_text}"
@@ -203,9 +220,11 @@ Relevant context:
             .strip()
         )
 
-    def get_summary(self, force_refresh: bool = False) -> str:
+    def get_summary(
+        self, force_refresh: bool = False, now: Optional[datetime] = None
+    ) -> str:
         """Return a descriptive summary of the agent."""
-        current_time = datetime.now()
+        current_time = datetime.now() if now is None else now
         since_refresh = (current_time - self.last_refreshed).seconds
         if (
             not self.summary
@@ -221,10 +240,13 @@ Relevant context:
             + f"\n{self.summary}"
         )
 
-    def get_full_header(self, force_refresh: bool = False) -> str:
+    def get_full_header(
+        self, force_refresh: bool = False, now: Optional[datetime] = None
+    ) -> str:
         """Return a full header of the agent's status, summary, and current time."""
-        summary = self.get_summary(force_refresh=force_refresh)
+        now = datetime.now() if now is None else now
-        current_time_str = datetime.now().strftime("%B %d, %Y, %I:%M %p")
+        summary = self.get_summary(force_refresh=force_refresh, now=now)
+        current_time_str = now.strftime("%B %d, %Y, %I:%M %p")
         return (
             f"{summary}\nIt is {current_time_str}.\n{self.name}'s status: {self.status}"
         )

langchain/experimental/generative_agents/memory.py

@@ -1,5 +1,6 @@
 import logging
 import re
+from datetime import datetime
 from typing import Any, Dict, List, Optional
 
 from langchain import LLMChain
@@ -7,6 +8,7 @@ from langchain.base_language import BaseLanguageModel
 from langchain.prompts import PromptTemplate
 from langchain.retrievers import TimeWeightedVectorStoreRetriever
 from langchain.schema import BaseMemory, Document
+from langchain.utils import mock_now
 
 logger = logging.getLogger(__name__)
 
@@ -44,6 +46,7 @@ class GenerativeAgentMemory(BaseMemory):
     relevant_memories_key: str = "relevant_memories"
     relevant_memories_simple_key: str = "relevant_memories_simple"
     most_recent_memories_key: str = "most_recent_memories"
+    now_key: str = "now"
     reflecting: bool = False
 
     def chain(self, prompt: PromptTemplate) -> LLMChain:
@@ -68,7 +71,9 @@ class GenerativeAgentMemory(BaseMemory):
         result = self.chain(prompt).run(observations=observation_str)
         return self._parse_list(result)
 
-    def _get_insights_on_topic(self, topic: str) -> List[str]:
+    def _get_insights_on_topic(
+        self, topic: str, now: Optional[datetime] = None
+    ) -> List[str]:
         """Generate 'insights' on a topic of reflection, based on pertinent memories."""
         prompt = PromptTemplate.from_template(
             "Statements about {topic}\n"
@@ -76,7 +81,7 @@ class GenerativeAgentMemory(BaseMemory):
             + "What 5 high-level insights can you infer from the above statements?"
             + " (example format: insight (because of 1, 5, 3))"
         )
-        related_memories = self.fetch_memories(topic)
+        related_memories = self.fetch_memories(topic, now=now)
         related_statements = "\n".join(
             [
                 f"{i+1}. {memory.page_content}"
@@ -89,16 +94,16 @@ class GenerativeAgentMemory(BaseMemory):
         # TODO: Parse the connections between memories and insights
         return self._parse_list(result)
 
-    def pause_to_reflect(self) -> List[str]:
+    def pause_to_reflect(self, now: Optional[datetime] = None) -> List[str]:
         """Reflect on recent observations and generate 'insights'."""
         if self.verbose:
             logger.info("Character is reflecting")
         new_insights = []
         topics = self._get_topics_of_reflection()
         for topic in topics:
-            insights = self._get_insights_on_topic(topic)
+            insights = self._get_insights_on_topic(topic, now=now)
             for insight in insights:
-                self.add_memory(insight)
+                self.add_memory(insight, now=now)
             new_insights.extend(insights)
         return new_insights
 
@@ -122,14 +127,16 @@ class GenerativeAgentMemory(BaseMemory):
         else:
             return 0.0
 
-    def add_memory(self, memory_content: str) -> List[str]:
+    def add_memory(
+        self, memory_content: str, now: Optional[datetime] = None
+    ) -> List[str]:
         """Add an observation or memory to the agent's memory."""
         importance_score = self._score_memory_importance(memory_content)
         self.aggregate_importance += importance_score
         document = Document(
             page_content=memory_content, metadata={"importance": importance_score}
         )
-        result = self.memory_retriever.add_documents([document])
+        result = self.memory_retriever.add_documents([document], current_time=now)
 
         # After an agent has processed a certain amount of memories (as measured by
         # aggregate importance), it is time to reflect on recent events to add
@@ -140,14 +147,20 @@ class GenerativeAgentMemory(BaseMemory):
             and not self.reflecting
         ):
             self.reflecting = True
-            self.pause_to_reflect()
+            self.pause_to_reflect(now=now)
             # Hack to clear the importance from reflection
             self.aggregate_importance = 0.0
             self.reflecting = False
         return result
 
-    def fetch_memories(self, observation: str) -> List[Document]:
+    def fetch_memories(
+        self, observation: str, now: Optional[datetime] = None
+    ) -> List[Document]:
         """Fetch related memories."""
+        if now is not None:
+            with mock_now(now):
+                return self.memory_retriever.get_relevant_documents(observation)
+        else:
             return self.memory_retriever.get_relevant_documents(observation)
 
     def format_memories_detail(self, relevant_memories: List[Document]) -> str:
@@ -183,9 +196,10 @@ class GenerativeAgentMemory(BaseMemory):
     def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
         """Return key-value pairs given the text input to the chain."""
         queries = inputs.get(self.queries_key)
+        now = inputs.get(self.now_key)
         if queries is not None:
             relevant_memories = [
-                mem for query in queries for mem in self.fetch_memories(query)
+                mem for query in queries for mem in self.fetch_memories(query, now=now)
             ]
             return {
                 self.relevant_memories_key: self.format_memories_detail(
@@ -205,12 +219,13 @@ class GenerativeAgentMemory(BaseMemory):
             }
         return {}
 
-    def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
+    def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, Any]) -> None:
         """Save the context of this model run to memory."""
         # TODO: fix the save memory key
         mem = outputs.get(self.add_memory_key)
+        now = outputs.get(self.now_key)
         if mem:
-            self.add_memory(mem)
+            self.add_memory(mem, now=now)
 
     def clear(self) -> None:
         """Clear memory contents."""

langchain/retrievers/time_weighted_retriever.py

@@ -1,6 +1,6 @@
 """Retriever that combines embedding similarity with recency in retrieving values."""
+import datetime
 from copy import deepcopy
-from datetime import datetime
 from typing import Any, Dict, List, Optional, Tuple
 
 from pydantic import BaseModel, Field
@@ -9,7 +9,7 @@ from langchain.schema import BaseRetriever, Document
 from langchain.vectorstores.base import VectorStore
 
 
-def _get_hours_passed(time: datetime, ref_time: datetime) -> float:
+def _get_hours_passed(time: datetime.datetime, ref_time: datetime.datetime) -> float:
     """Get the hours passed between two datetime objects."""
     return (time - ref_time).total_seconds() / 3600
 
@@ -51,7 +51,7 @@ class TimeWeightedVectorStoreRetriever(BaseRetriever, BaseModel):
         self,
         document: Document,
         vector_relevance: Optional[float],
-        current_time: datetime,
+        current_time: datetime.datetime,
     ) -> float:
         """Return the combined score for a document."""
         hours_passed = _get_hours_passed(
@@ -82,7 +82,7 @@ class TimeWeightedVectorStoreRetriever(BaseRetriever, BaseModel):
 
     def get_relevant_documents(self, query: str) -> List[Document]:
         """Return documents that are relevant to the query."""
-        current_time = datetime.now()
+        current_time = datetime.datetime.now()
         docs_and_scores = {
             doc.metadata["buffer_idx"]: (doc, self.default_salience)
             for doc in self.memory_stream[-self.k :]
@@ -96,7 +96,6 @@ class TimeWeightedVectorStoreRetriever(BaseRetriever, BaseModel):
         rescored_docs.sort(key=lambda x: x[1], reverse=True)
         result = []
         # Ensure frequently accessed memories aren't forgotten
-        current_time = datetime.now()
         for doc, _ in rescored_docs[: self.k]:
             # TODO: Update vector store doc once `update` method is exposed.
             buffered_doc = self.memory_stream[doc.metadata["buffer_idx"]]
@@ -110,7 +109,7 @@ class TimeWeightedVectorStoreRetriever(BaseRetriever, BaseModel):
 
     def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
         """Add documents to vectorstore."""
-        current_time = kwargs.get("current_time", datetime.now())
+        current_time = kwargs.get("current_time", datetime.datetime.now())
         # Avoid mutating input documents
         dup_docs = [deepcopy(d) for d in documents]
         for i, doc in enumerate(dup_docs):
@@ -126,7 +125,7 @@ class TimeWeightedVectorStoreRetriever(BaseRetriever, BaseModel):
         self, documents: List[Document], **kwargs: Any
     ) -> List[str]:
         """Add documents to vectorstore."""
-        current_time = kwargs.get("current_time", datetime.now())
+        current_time = kwargs.get("current_time", datetime.datetime.now())
         # Avoid mutating input documents
         dup_docs = [deepcopy(d) for d in documents]
         for i, doc in enumerate(dup_docs):

langchain/utils.py

@@ -1,4 +1,6 @@
 """Generic utility functions."""
+import contextlib
+import datetime
 import os
 from typing import Any, Callable, Dict, Optional, Tuple
 
@@ -78,3 +80,34 @@ def stringify_dict(data: dict) -> str:
     for key, value in data.items():
         text += key + ": " + stringify_value(value) + "\n"
     return text
+
+
+@contextlib.contextmanager
+def mock_now(dt_value):  # type: ignore
+    """Context manager for mocking out datetime.now() in unit tests.
+    Example:
+    with mock_now(datetime.datetime(2011, 2, 3, 10, 11)):
+        assert datetime.datetime.now() == datetime.datetime(2011, 2, 3, 10, 11)
+    """
+
+    class MockDateTime(datetime.datetime):
+        @classmethod
+        def now(cls):  # type: ignore
+            # Create a copy of dt_value.
+            return datetime.datetime(
+                dt_value.year,
+                dt_value.month,
+                dt_value.day,
+                dt_value.hour,
+                dt_value.minute,
+                dt_value.second,
+                dt_value.microsecond,
+                dt_value.tzinfo,
+            )
+
+    real_datetime = datetime.datetime
+    datetime.datetime = MockDateTime
+    try:
+        yield datetime.datetime
+    finally:
+        datetime.datetime = real_datetime