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# Source code for langchain.memory.entity

    import logging
    from abc import ABC, abstractmethod
    from itertools import islice
    from typing import Any, Dict, Iterable, List, Optional

    from langchain_core.language_models import BaseLanguageModel
    from langchain_core.messages import BaseMessage, get_buffer_string
    from langchain_core.prompts import BasePromptTemplate
    from langchain_core.pydantic_v1 import BaseModel, Field

    from langchain.chains.llm import LLMChain
    from langchain.memory.chat_memory import BaseChatMemory
    from langchain.memory.prompt import (
        ENTITY_EXTRACTION_PROMPT,
        ENTITY_SUMMARIZATION_PROMPT,
    )
    from langchain.memory.utils import get_prompt_input_key

    logger = logging.getLogger(__name__)

    class BaseEntityStore(BaseModel, ABC):
        """Abstract base class for Entity store."""

        @abstractmethod
        def get(self, key: str, default: Optional[str] = None) -> Optional[str]:
            """Get entity value from store."""
            pass

        @abstractmethod
        def set(self, key: str, value: Optional[str]) -> None:
            """Set entity value in store."""
            pass

        @abstractmethod
        def delete(self, key: str) -> None:
            """Delete entity value from store."""
            pass

        @abstractmethod
        def exists(self, key: str) -> bool:
            """Check if entity exists in store."""
            pass

        @abstractmethod
        def clear(self) -> None:
            """Delete all entities from store."""
            pass

    class InMemoryEntityStore(BaseEntityStore):
        """In-memory Entity store."""

        store: Dict[str, Optional[str]] = {}

        def get(self, key: str, default: Optional[str] = None) -> Optional[str]:
            return self.store.get(key, default)

        def set(self, key: str, value: Optional[str]) -> None:
            self.store[key] = value

        def delete(self, key: str) -> None:
            del self.store[key]

        def exists(self, key: str) -> bool:
            return key in self.store

        def clear(self) -> None:
            return self.store.clear()

    class UpstashRedisEntityStore(BaseEntityStore):
        """Upstash Redis backed Entity store.

        Entities get a TTL of 1 day by default, and
        that TTL is extended by 3 days every time the entity is read back.
        """

        def __init__(
            self,
            session_id: str = "default",
            url: str = "",
            token: str = "",
            key_prefix: str = "memory_store",
            ttl: Optional[int] = 60 * 60 * 24,
            recall_ttl: Optional[int] = 60 * 60 * 24 * 3,
            *args: Any,
            **kwargs: Any,
        ):
            try:
                from upstash_redis import Redis
            except ImportError:
                raise ImportError(
                    "Could not import upstash_redis python package. "
                    "Please install it with `pip install upstash_redis`."
                )

            super().__init__(*args, **kwargs)

            try:
                self.redis_client = Redis(url=url, token=token)
            except Exception:
                logger.error("Upstash Redis instance could not be initiated.")

            self.session_id = session_id
            self.key_prefix = key_prefix
            self.ttl = ttl
            self.recall_ttl = recall_ttl or ttl

        @property
        def full_key_prefix(self) -> str:
            return f"{self.key_prefix}:{self.session_id}"

        def get(self, key: str, default: Optional[str] = None) -> Optional[str]:
            res = (
                self.redis_client.getex(f"{self.full_key_prefix}:{key}", ex=self.recall_ttl)
                or default
                or ""
            )
            logger.debug(f"Upstash Redis MEM get '{self.full_key_prefix}:{key}': '{res}'")
            return res

        def set(self, key: str, value: Optional[str]) -> None:
            if not value:
                return self.delete(key)
            self.redis_client.set(f"{self.full_key_prefix}:{key}", value, ex=self.ttl)
            logger.debug(
                f"Redis MEM set '{self.full_key_prefix}:{key}': '{value}' EX {self.ttl}"
            )

        def delete(self, key: str) -> None:
            self.redis_client.delete(f"{self.full_key_prefix}:{key}")

        def exists(self, key: str) -> bool:
            return self.redis_client.exists(f"{self.full_key_prefix}:{key}") == 1

        def clear(self) -> None:
            def scan_and_delete(cursor: int) -> int:
                cursor, keys_to_delete = self.redis_client.scan(
                    cursor, f"{self.full_key_prefix}:*"
                )
                self.redis_client.delete(*keys_to_delete)
                return cursor

            cursor = scan_and_delete(0)
            while cursor != 0:
                scan_and_delete(cursor)

    class RedisEntityStore(BaseEntityStore):
        """Redis-backed Entity store.

        Entities get a TTL of 1 day by default, and
        that TTL is extended by 3 days every time the entity is read back.
        """

        redis_client: Any
        session_id: str = "default"
        key_prefix: str = "memory_store"
        ttl: Optional[int] = 60 * 60 * 24
        recall_ttl: Optional[int] = 60 * 60 * 24 * 3

        def __init__(
            self,
            session_id: str = "default",
            url: str = "redis://localhost:6379/0",
            key_prefix: str = "memory_store",
            ttl: Optional[int] = 60 * 60 * 24,
            recall_ttl: Optional[int] = 60 * 60 * 24 * 3,
            *args: Any,
            **kwargs: Any,
        ):
            try:
                import redis
            except ImportError:
                raise ImportError(
                    "Could not import redis python package. "
                    "Please install it with `pip install redis`."
                )

            super().__init__(*args, **kwargs)

            try:
                from langchain_community.utilities.redis import get_client
            except ImportError:
                raise ImportError(
                    "Could not import langchain_community.utilities.redis.get_client. "
                    "Please install it with `pip install langchain-community`."
                )

            try:
                self.redis_client = get_client(redis_url=url, decode_responses=True)
            except redis.exceptions.ConnectionError as error:
                logger.error(error)

            self.session_id = session_id
            self.key_prefix = key_prefix
            self.ttl = ttl
            self.recall_ttl = recall_ttl or ttl

        @property
        def full_key_prefix(self) -> str:
            return f"{self.key_prefix}:{self.session_id}"

        def get(self, key: str, default: Optional[str] = None) -> Optional[str]:
            res = (
                self.redis_client.getex(f"{self.full_key_prefix}:{key}", ex=self.recall_ttl)
                or default
                or ""
            )
            logger.debug(f"REDIS MEM get '{self.full_key_prefix}:{key}': '{res}'")
            return res

        def set(self, key: str, value: Optional[str]) -> None:
            if not value:
                return self.delete(key)
            self.redis_client.set(f"{self.full_key_prefix}:{key}", value, ex=self.ttl)
            logger.debug(
                f"REDIS MEM set '{self.full_key_prefix}:{key}': '{value}' EX {self.ttl}"
            )

        def delete(self, key: str) -> None:
            self.redis_client.delete(f"{self.full_key_prefix}:{key}")

        def exists(self, key: str) -> bool:
            return self.redis_client.exists(f"{self.full_key_prefix}:{key}") == 1

        def clear(self) -> None:
            # iterate a list in batches of size batch_size
            def batched(iterable: Iterable[Any], batch_size: int) -> Iterable[Any]:
                iterator = iter(iterable)
                while batch := list(islice(iterator, batch_size)):
                    yield batch

            for keybatch in batched(
                self.redis_client.scan_iter(f"{self.full_key_prefix}:*"), 500
            ):
                self.redis_client.delete(*keybatch)

    class SQLiteEntityStore(BaseEntityStore):
        """SQLite-backed Entity store"""

        session_id: str = "default"
        table_name: str = "memory_store"
        conn: Any = None

        class Config:
            """Configuration for this pydantic object."""

            arbitrary_types_allowed = True

        def __init__(
            self,
            session_id: str = "default",
            db_file: str = "entities.db",
            table_name: str = "memory_store",
            *args: Any,
            **kwargs: Any,
        ):
            try:
                import sqlite3
            except ImportError:
                raise ImportError(
                    "Could not import sqlite3 python package. "
                    "Please install it with `pip install sqlite3`."
                )
            super().__init__(*args, **kwargs)

            self.conn = sqlite3.connect(db_file)
            self.session_id = session_id
            self.table_name = table_name
            self._create_table_if_not_exists()

        @property
        def full_table_name(self) -> str:
            return f"{self.table_name}_{self.session_id}"

        def _create_table_if_not_exists(self) -> None:
            create_table_query = f"""
                CREATE TABLE IF NOT EXISTS {self.full_table_name} (
                    key TEXT PRIMARY KEY,
                    value TEXT
                )
            """
            with self.conn:
                self.conn.execute(create_table_query)

        def get(self, key: str, default: Optional[str] = None) -> Optional[str]:
            query = f"""
                SELECT value
                FROM {self.full_table_name}
                WHERE key = ?
            """
            cursor = self.conn.execute(query, (key,))
            result = cursor.fetchone()
            if result is not None:
                value = result[0]
                return value
            return default

        def set(self, key: str, value: Optional[str]) -> None:
            if not value:
                return self.delete(key)
            query = f"""
                INSERT OR REPLACE INTO {self.full_table_name} (key, value)
                VALUES (?, ?)
            """
            with self.conn:
                self.conn.execute(query, (key, value))

        def delete(self, key: str) -> None:
            query = f"""
                DELETE FROM {self.full_table_name}
                WHERE key = ?
            """
            with self.conn:
                self.conn.execute(query, (key,))

        def exists(self, key: str) -> bool:
            query = f"""
                SELECT 1
                FROM {self.full_table_name}
                WHERE key = ?
                LIMIT 1
            """
            cursor = self.conn.execute(query, (key,))
            result = cursor.fetchone()
            return result is not None

        def clear(self) -> None:
            query = f"""
                DELETE FROM {self.full_table_name}
            """
            with self.conn:
                self.conn.execute(query)

    class ConversationEntityMemory(BaseChatMemory):
        """Entity extractor & summarizer memory.

        Extracts named entities from the recent chat history and generates summaries.
        With a swappable entity store, persisting entities across conversations.
        Defaults to an in-memory entity store, and can be swapped out for a Redis,
        SQLite, or other entity store.
        """

        human_prefix: str = "Human"
        ai_prefix: str = "AI"
        llm: BaseLanguageModel
        entity_extraction_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT
        entity_summarization_prompt: BasePromptTemplate = ENTITY_SUMMARIZATION_PROMPT

        # Cache of recently detected entity names, if any
        # It is updated when load_memory_variables is called:
        entity_cache: List[str] = []

        # Number of recent message pairs to consider when updating entities:
        k: int = 3

        chat_history_key: str = "history"

        # Store to manage entity-related data:
        entity_store: BaseEntityStore = Field(default_factory=InMemoryEntityStore)

        @property
        def buffer(self) -> List[BaseMessage]:
            """Access chat memory messages."""
            return self.chat_memory.messages

        @property
        def memory_variables(self) -> List[str]:
            """Will always return list of memory variables.

            :meta private:
            """
            return ["entities", self.chat_history_key]

        def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
            """
            Returns chat history and all generated entities with summaries if available,
            and updates or clears the recent entity cache.

            New entity name can be found when calling this method, before the entity
            summaries are generated, so the entity cache values may be empty if no entity
            descriptions are generated yet.
            """

            # Create an LLMChain for predicting entity names from the recent chat history:
            chain = LLMChain(llm=self.llm, prompt=self.entity_extraction_prompt)

            if self.input_key is None:
                prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)
            else:
                prompt_input_key = self.input_key

            # Extract an arbitrary window of the last message pairs from
            # the chat history, where the hyperparameter k is the
            # number of message pairs:
            buffer_string = get_buffer_string(
                self.buffer[-self.k * 2 :],
                human_prefix=self.human_prefix,
                ai_prefix=self.ai_prefix,
            )

            # Generates a comma-separated list of named entities,
            # e.g. "Jane, White House, UFO"
            # or "NONE" if no named entities are extracted:
            output = chain.predict(
                history=buffer_string,
                input=inputs[prompt_input_key],
            )

            # If no named entities are extracted, assigns an empty list.
            if output.strip() == "NONE":
                entities = []
            else:
                # Make a list of the extracted entities:
                entities = [w.strip() for w in output.split(",")]

            # Make a dictionary of entities with summary if exists:
            entity_summaries = {}

            for entity in entities:
                entity_summaries[entity] = self.entity_store.get(entity, "")

            # Replaces the entity name cache with the most recently discussed entities,
            # or if no entities were extracted, clears the cache:
            self.entity_cache = entities

            # Should we return as message objects or as a string?
            if self.return_messages:
                # Get last `k` pair of chat messages:
                buffer: Any = self.buffer[-self.k * 2 :]
            else:
                # Reuse the string we made earlier:
                buffer = buffer_string

            return {
                self.chat_history_key: buffer,
                "entities": entity_summaries,
            }

        def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
            """
            Save context from this conversation history to the entity store.

            Generates a summary for each entity in the entity cache by prompting
            the model, and saves these summaries to the entity store.
            """

            super().save_context(inputs, outputs)

            if self.input_key is None:
                prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)
            else:
                prompt_input_key = self.input_key

            # Extract an arbitrary window of the last message pairs from
            # the chat history, where the hyperparameter k is the
            # number of message pairs:
            buffer_string = get_buffer_string(
                self.buffer[-self.k * 2 :],
                human_prefix=self.human_prefix,
                ai_prefix=self.ai_prefix,
            )

            input_data = inputs[prompt_input_key]

            # Create an LLMChain for predicting entity summarization from the context
            chain = LLMChain(llm=self.llm, prompt=self.entity_summarization_prompt)

            # Generate new summaries for entities and save them in the entity store
            for entity in self.entity_cache:
                # Get existing summary if it exists
                existing_summary = self.entity_store.get(entity, "")
                output = chain.predict(
                    summary=existing_summary,
                    entity=entity,
                    history=buffer_string,
                    input=input_data,
                )
                # Save the updated summary to the entity store
                self.entity_store.set(entity, output.strip())

        def clear(self) -> None:
            """Clear memory contents."""
            self.chat_memory.clear()
            self.entity_cache.clear()
            self.entity_store.clear()