viruPVS/langchain-docs huggingface dataset:
"""In-memory Entity store."""
    store: Dict[str, Optional[str]] = {}
[docs]    def get(self, key: str, default: Optional[str] = None) -> Optional[str]:
        return self.store.get(key, default)
[docs]    def set(self, key: str, value: Optional[str]) -> None:
        self.store[key] = value
[docs]    def delete(self, key: str) -> None:
        del self.store[key]
[docs]    def exists(self, key: str) -> bool:
        return key in self.store
[docs]    def clear(self) -> None:
        return self.store.clear()
[docs]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:
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}"
[docs]    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
[docs]    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}"
        )
[docs]    def delete(self, key: str) -> None:
        self.redis_client.delete(f"{self.full_key_prefix}:{key}")
[docs]    def exists(self, key: str) -> bool:
        return self.redis_client.exists(f"{self.full_key_prefix}:{key}") == 1
[docs]    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}:*"
            )
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)
[docs]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:
            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.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}"
[docs]    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
[docs]    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}"
        )
[docs]    def delete(self, key: str) -> None:
        self.redis_client.delete(f"{self.full_key_prefix}:{key}")
[docs]    def exists(self, key: str) -> bool:
        return self.redis_client.exists(f"{self.full_key_prefix}:{key}") == 1
[docs]    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)
[docs]class SQLiteEntityStore(BaseEntityStore):
    """SQLite-backed Entity store"""
    session_id: str = "default"
    table_name: str = "memory_store"
    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)
[docs]    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
if result is not None:
            value = result[0]
            return value
        return default
[docs]    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))
[docs]    def delete(self, key: str) -> None:
        query = f"""
            DELETE FROM {self.full_table_name}
            WHERE key = ?
        """
        with self.conn:
            self.conn.execute(query, (key,))
[docs]    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
[docs]    def clear(self) -> None:
        query = f"""
            DELETE FROM {self.full_table_name}
        """
        with self.conn:
            self.conn.execute(query)
[docs]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
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]
[docs]    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
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,
        }
"entities": entity_summaries,
        }
[docs]    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())
[docs]    def clear(self) -> None:
        """Clear memory contents."""
        self.chat_memory.clear()
        self.entity_cache.clear()
        self.entity_store.clear()
Source code for langchain.memory.utils
from typing import Any, Dict, List
[docs]def get_prompt_input_key(inputs: Dict[str, Any], memory_variables: List[str]) -> str:
    """
    Get the prompt input key.
    Args:
        inputs: Dict[str, Any]
        memory_variables: List[str]
    Returns:
        A prompt input key.
    """
    # "stop" is a special key that can be passed as input but is not used to
    # format the prompt.
    prompt_input_keys = list(set(inputs).difference(memory_variables + ["stop"]))
    if len(prompt_input_keys) != 1:
        raise ValueError(f"One input key expected got {prompt_input_keys}")
    return prompt_input_keys[0]
Source code for langchain.memory.buffer_window
from typing import Any, Dict, List, Union
from langchain_core.messages import BaseMessage, get_buffer_string
from langchain.memory.chat_memory import BaseChatMemory
[docs]class ConversationBufferWindowMemory(BaseChatMemory):
    """Buffer for storing conversation memory inside a limited size window."""
    human_prefix: str = "Human"
    ai_prefix: str = "AI"
    memory_key: str = "history"  #: :meta private:
    k: int = 5
    """Number of messages to store in buffer."""
    @property
    def buffer(self) -> Union[str, List[BaseMessage]]:
        """String buffer of memory."""
        return self.buffer_as_messages if self.return_messages else self.buffer_as_str
    @property
    def buffer_as_str(self) -> str:
        """Exposes the buffer as a string in case return_messages is True."""
        messages = self.chat_memory.messages[-self.k * 2 :] if self.k > 0 else []
        return get_buffer_string(
            messages,
            human_prefix=self.human_prefix,
            ai_prefix=self.ai_prefix,
        )
    @property
    def buffer_as_messages(self) -> List[BaseMessage]:
        """Exposes the buffer as a list of messages in case return_messages is False."""
        return self.chat_memory.messages[-self.k * 2 :] if self.k > 0 else []
    @property
    def memory_variables(self) -> List[str]:
        """Will always return list of memory variables.
        :meta private:
        """
        return [self.memory_key]
[docs]    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
        """Return history buffer."""
        return {self.memory_key: self.buffer}
Source code for langchain.memory.vectorstore
"""Class for a VectorStore-backed memory object."""
from typing import Any, Dict, List, Optional, Sequence, Union
from langchain_core.documents import Document
from langchain_core.pydantic_v1 import Field
from langchain_core.vectorstores import VectorStoreRetriever
from langchain.memory.chat_memory import BaseMemory
from langchain.memory.utils import get_prompt_input_key
[docs]class VectorStoreRetrieverMemory(BaseMemory):
    """VectorStoreRetriever-backed memory."""
    retriever: VectorStoreRetriever = Field(exclude=True)
    """VectorStoreRetriever object to connect to."""
    memory_key: str = "history"  #: :meta private:
    """Key name to locate the memories in the result of load_memory_variables."""
    input_key: Optional[str] = None
    """Key name to index the inputs to load_memory_variables."""
    return_docs: bool = False
    """Whether or not to return the result of querying the database directly."""
    exclude_input_keys: Sequence[str] = Field(default_factory=tuple)
    """Input keys to exclude in addition to memory key when constructing the document"""
    @property
    def memory_variables(self) -> List[str]:
        """The list of keys emitted from the load_memory_variables method."""
        return [self.memory_key]
    def _get_prompt_input_key(self, inputs: Dict[str, Any]) -> str:
        """Get the input key for the prompt."""
        if self.input_key is None:
            return get_prompt_input_key(inputs, self.memory_variables)
        return self.input_key
[docs]    def load_memory_variables(
        self, inputs: Dict[str, Any]
    ) -> Dict[str, Union[List[Document], str]]:
        """Return history buffer."""
"""Return history buffer."""
        input_key = self._get_prompt_input_key(inputs)
        query = inputs[input_key]
        docs = self.retriever.get_relevant_documents(query)
        result: Union[List[Document], str]
        if not self.return_docs:
            result = "\n".join([doc.page_content for doc in docs])
        else:
            result = docs
        return {self.memory_key: result}
    def _form_documents(
        self, inputs: Dict[str, Any], outputs: Dict[str, str]
    ) -> List[Document]:
        """Format context from this conversation to buffer."""
        # Each document should only include the current turn, not the chat history
        exclude = set(self.exclude_input_keys)
        exclude.add(self.memory_key)
        filtered_inputs = {k: v for k, v in inputs.items() if k not in exclude}
        texts = [
            f"{k}: {v}"
            for k, v in list(filtered_inputs.items()) + list(outputs.items())
        ]
        page_content = "\n".join(texts)
        return [Document(page_content=page_content)]
[docs]    def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
        """Save context from this conversation to buffer."""
        documents = self._form_documents(inputs, outputs)
        self.retriever.add_documents(documents)
[docs]    def clear(self) -> None:
        """Nothing to clear."""
Source code for langchain.memory.token_buffer
from typing import Any, Dict, List
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import BaseMessage, get_buffer_string
from langchain.memory.chat_memory import BaseChatMemory
[docs]class ConversationTokenBufferMemory(BaseChatMemory):
    """Conversation chat memory with token limit."""
    human_prefix: str = "Human"
    ai_prefix: str = "AI"
    llm: BaseLanguageModel
    memory_key: str = "history"
    max_token_limit: int = 2000
    @property
    def buffer(self) -> Any:
        """String buffer of memory."""
        return self.buffer_as_messages if self.return_messages else self.buffer_as_str
    @property
    def buffer_as_str(self) -> str:
        """Exposes the buffer as a string in case return_messages is False."""
        return get_buffer_string(
            self.chat_memory.messages,
            human_prefix=self.human_prefix,
            ai_prefix=self.ai_prefix,
        )
    @property
    def buffer_as_messages(self) -> List[BaseMessage]:
        """Exposes the buffer as a list of messages in case return_messages is True."""
        return self.chat_memory.messages
    @property
    def memory_variables(self) -> List[str]:
        """Will always return list of memory variables.
        :meta private:
        """
        return [self.memory_key]
[docs]    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
        """Return history buffer."""
        return {self.memory_key: self.buffer}
[docs]    def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
        """Save context from this conversation to buffer. Pruned."""
"""Save context from this conversation to buffer. Pruned."""
        super().save_context(inputs, outputs)
        # Prune buffer if it exceeds max token limit
        buffer = self.chat_memory.messages
        curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
        if curr_buffer_length > self.max_token_limit:
            pruned_memory = []
            while curr_buffer_length > self.max_token_limit:
                pruned_memory.append(buffer.pop(0))
                curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
Source code for langchain.memory.buffer
from typing import Any, Dict, List, Optional
from langchain_core.messages import BaseMessage, get_buffer_string
from langchain_core.pydantic_v1 import root_validator
from langchain.memory.chat_memory import BaseChatMemory, BaseMemory
from langchain.memory.utils import get_prompt_input_key
[docs]class ConversationBufferMemory(BaseChatMemory):
    """Buffer for storing conversation memory."""
    human_prefix: str = "Human"
    ai_prefix: str = "AI"
    memory_key: str = "history"  #: :meta private:
    @property
    def buffer(self) -> Any:
        """String buffer of memory."""
        return self.buffer_as_messages if self.return_messages else self.buffer_as_str
    @property
    def buffer_as_str(self) -> str:
        """Exposes the buffer as a string in case return_messages is True."""
        return get_buffer_string(
            self.chat_memory.messages,
            human_prefix=self.human_prefix,
            ai_prefix=self.ai_prefix,
        )
    @property
    def buffer_as_messages(self) -> List[BaseMessage]:
        """Exposes the buffer as a list of messages in case return_messages is False."""
        return self.chat_memory.messages
    @property
    def memory_variables(self) -> List[str]:
        """Will always return list of memory variables.
        :meta private:
        """
        return [self.memory_key]
[docs]    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
        """Return history buffer."""
        return {self.memory_key: self.buffer}
[docs]class ConversationStringBufferMemory(BaseMemory):
    """Buffer for storing conversation memory."""
    human_prefix: str = "Human"
    ai_prefix: str = "AI"