initial commit

venv/Lib/site-packages/langchain_community/callbacks/streamlit/__init__.py

@@ -0,0 +1,82 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Optional
+
+from langchain_core.callbacks import BaseCallbackHandler
+
+from langchain_community.callbacks.streamlit.streamlit_callback_handler import (
+    LLMThoughtLabeler as LLMThoughtLabeler,
+)
+from langchain_community.callbacks.streamlit.streamlit_callback_handler import (
+    StreamlitCallbackHandler as _InternalStreamlitCallbackHandler,
+)
+
+if TYPE_CHECKING:
+    from streamlit.delta_generator import DeltaGenerator
+
+
+def StreamlitCallbackHandler(
+    parent_container: DeltaGenerator,
+    *,
+    max_thought_containers: int = 4,
+    expand_new_thoughts: bool = True,
+    collapse_completed_thoughts: bool = True,
+    thought_labeler: Optional[LLMThoughtLabeler] = None,
+) -> BaseCallbackHandler:
+    """Callback Handler that writes to a Streamlit app.
+
+    This CallbackHandler is geared towards
+    use with a LangChain Agent; it displays the Agent's LLM and tool-usage "thoughts"
+    inside a series of Streamlit expanders.
+
+    Parameters
+    ----------
+    parent_container
+        The `st.container` that will contain all the Streamlit elements that the
+        Handler creates.
+    max_thought_containers
+        The max number of completed LLM thought containers to show at once. When this
+        threshold is reached, a new thought will cause the oldest thoughts to be
+        collapsed into a "History" expander. Defaults to 4.
+    expand_new_thoughts
+        Each LLM "thought" gets its own `st.expander`. This param controls whether that
+        expander is expanded by default. Defaults to True.
+    collapse_completed_thoughts
+        If True, LLM thought expanders will be collapsed when completed.
+        Defaults to True.
+    thought_labeler
+        An optional custom LLMThoughtLabeler instance. If unspecified, the handler
+        will use the default thought labeling logic. Defaults to None.
+
+    Returns
+    -------
+    A new StreamlitCallbackHandler instance.
+
+    Note that this is an "auto-updating" API: if the installed version of Streamlit
+    has a more recent StreamlitCallbackHandler implementation, an instance of that class
+    will be used.
+
+    """
+    # If we're using a version of Streamlit that implements StreamlitCallbackHandler,
+    # delegate to it instead of using our built-in handler. The official handler is
+    # guaranteed to support the same set of kwargs.
+    try:
+        from streamlit.external.langchain import (
+            StreamlitCallbackHandler as OfficialStreamlitCallbackHandler,
+        )
+
+        return OfficialStreamlitCallbackHandler(
+            parent_container,
+            max_thought_containers=max_thought_containers,
+            expand_new_thoughts=expand_new_thoughts,
+            collapse_completed_thoughts=collapse_completed_thoughts,
+            thought_labeler=thought_labeler,
+        )
+    except ImportError:
+        return _InternalStreamlitCallbackHandler(
+            parent_container,
+            max_thought_containers=max_thought_containers,
+            expand_new_thoughts=expand_new_thoughts,
+            collapse_completed_thoughts=collapse_completed_thoughts,
+            thought_labeler=thought_labeler,
+        )

venv/Lib/site-packages/langchain_community/callbacks/streamlit/mutable_expander.py

@@ -0,0 +1,156 @@
+from __future__ import annotations
+
+from enum import Enum
+from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional
+
+if TYPE_CHECKING:
+    from streamlit.delta_generator import DeltaGenerator
+    from streamlit.type_util import SupportsStr
+
+
+class ChildType(Enum):
+    """Enumerator of the child type."""
+
+    MARKDOWN = "MARKDOWN"
+    EXCEPTION = "EXCEPTION"
+
+
+class ChildRecord(NamedTuple):
+    """Child record as a NamedTuple."""
+
+    type: ChildType
+    kwargs: Dict[str, Any]
+    dg: DeltaGenerator
+
+
+class MutableExpander:
+    """Streamlit expander that can be renamed and dynamically expanded/collapsed."""
+
+    def __init__(self, parent_container: DeltaGenerator, label: str, expanded: bool):
+        """Create a new MutableExpander.
+
+        Parameters
+        ----------
+        parent_container
+            The `st.container` that the expander will be created inside.
+
+            The expander transparently deletes and recreates its underlying
+            `st.expander` instance when its label changes, and it uses
+            `parent_container` to ensure it recreates this underlying expander in the
+            same location onscreen.
+        label
+            The expander's initial label.
+        expanded
+            The expander's initial `expanded` value.
+        """
+        self._label = label
+        self._expanded = expanded
+        self._parent_cursor = parent_container.empty()
+        self._container = self._parent_cursor.expander(label, expanded)
+        self._child_records: List[ChildRecord] = []
+
+    @property
+    def label(self) -> str:
+        """Expander's label string."""
+        return self._label
+
+    @property
+    def expanded(self) -> bool:
+        """True if the expander was created with `expanded=True`."""
+        return self._expanded
+
+    def clear(self) -> None:
+        """Remove the container and its contents entirely. A cleared container can't
+        be reused.
+        """
+        self._container = self._parent_cursor.empty()
+        self._child_records.clear()
+
+    def append_copy(self, other: MutableExpander) -> None:
+        """Append a copy of another MutableExpander's children to this
+        MutableExpander.
+        """
+        other_records = other._child_records.copy()
+        for record in other_records:
+            self._create_child(record.type, record.kwargs)
+
+    def update(
+        self, *, new_label: Optional[str] = None, new_expanded: Optional[bool] = None
+    ) -> None:
+        """Change the expander's label and expanded state"""
+        if new_label is None:
+            new_label = self._label
+        if new_expanded is None:
+            new_expanded = self._expanded
+
+        if self._label == new_label and self._expanded == new_expanded:
+            # No change!
+            return
+
+        self._label = new_label
+        self._expanded = new_expanded
+        self._container = self._parent_cursor.expander(new_label, new_expanded)
+
+        prev_records = self._child_records
+        self._child_records = []
+
+        # Replay all children into the new container
+        for record in prev_records:
+            self._create_child(record.type, record.kwargs)
+
+    def markdown(
+        self,
+        body: SupportsStr,
+        unsafe_allow_html: bool = False,
+        *,
+        help: Optional[str] = None,
+        index: Optional[int] = None,
+    ) -> int:
+        """Add a Markdown element to the container and return its index."""
+        kwargs = {"body": body, "unsafe_allow_html": unsafe_allow_html, "help": help}
+        new_dg = self._get_dg(index).markdown(**kwargs)
+        record = ChildRecord(ChildType.MARKDOWN, kwargs, new_dg)
+        return self._add_record(record, index)
+
+    def exception(
+        self, exception: BaseException, *, index: Optional[int] = None
+    ) -> int:
+        """Add an Exception element to the container and return its index."""
+        kwargs = {"exception": exception}
+        new_dg = self._get_dg(index).exception(**kwargs)
+        record = ChildRecord(ChildType.EXCEPTION, kwargs, new_dg)
+        return self._add_record(record, index)
+
+    def _create_child(self, type: ChildType, kwargs: Dict[str, Any]) -> None:
+        """Create a new child with the given params"""
+        if type == ChildType.MARKDOWN:
+            self.markdown(**kwargs)
+        elif type == ChildType.EXCEPTION:
+            self.exception(**kwargs)
+        else:
+            raise RuntimeError(f"Unexpected child type {type}")
+
+    def _add_record(self, record: ChildRecord, index: Optional[int]) -> int:
+        """Add a ChildRecord to self._children. If `index` is specified, replace
+        the existing record at that index. Otherwise, append the record to the
+        end of the list.
+
+        Return the index of the added record.
+        """
+        if index is not None:
+            # Replace existing child
+            self._child_records[index] = record
+            return index
+
+        # Append new child
+        self._child_records.append(record)
+        return len(self._child_records) - 1
+
+    def _get_dg(self, index: Optional[int]) -> DeltaGenerator:
+        if index is not None:
+            # Existing index: reuse child's DeltaGenerator
+            assert 0 <= index < len(self._child_records), f"Bad index: {index}"
+            return self._child_records[index].dg
+
+        # No index: use container's DeltaGenerator
+        return self._container

venv/Lib/site-packages/langchain_community/callbacks/streamlit/streamlit_callback_handler.py

@@ -0,0 +1,419 @@
+"""Callback Handler that prints to streamlit."""
+
+from __future__ import annotations
+
+from enum import Enum
+from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional
+
+from langchain_core.agents import AgentAction, AgentFinish
+from langchain_core.callbacks import BaseCallbackHandler
+from langchain_core.outputs import LLMResult
+
+from langchain_community.callbacks.streamlit.mutable_expander import MutableExpander
+
+if TYPE_CHECKING:
+    from streamlit.delta_generator import DeltaGenerator
+
+
+def _convert_newlines(text: str) -> str:
+    """Convert newline characters to markdown newline sequences
+    (space, space, newline).
+    """
+    return text.replace("\n", "  \n")
+
+
+CHECKMARK_EMOJI = "✅"
+THINKING_EMOJI = ":thinking_face:"
+HISTORY_EMOJI = ":books:"
+EXCEPTION_EMOJI = "⚠️"
+
+
+class LLMThoughtState(Enum):
+    """Enumerator of the LLMThought state."""
+
+    # The LLM is thinking about what to do next. We don't know which tool we'll run.
+    THINKING = "THINKING"
+    # The LLM has decided to run a tool. We don't have results from the tool yet.
+    RUNNING_TOOL = "RUNNING_TOOL"
+    # We have results from the tool.
+    COMPLETE = "COMPLETE"
+
+
+class ToolRecord(NamedTuple):
+    """Tool record as a NamedTuple."""
+
+    name: str
+    input_str: str
+
+
+class LLMThoughtLabeler:
+    """
+    Generates markdown labels for LLMThought containers. Pass a custom
+    subclass of this to StreamlitCallbackHandler to override its default
+    labeling logic.
+    """
+
+    @staticmethod
+    def get_initial_label() -> str:
+        """Return the markdown label for a new LLMThought that doesn't have
+        an associated tool yet.
+        """
+        return f"{THINKING_EMOJI} **Thinking...**"
+
+    @staticmethod
+    def get_tool_label(tool: ToolRecord, is_complete: bool) -> str:
+        """Return the label for an LLMThought that has an associated
+        tool.
+
+        Parameters
+        ----------
+        tool
+            The tool's ToolRecord
+
+        is_complete
+            True if the thought is complete; False if the thought
+            is still receiving input.
+
+        Returns
+        -------
+        The markdown label for the thought's container.
+
+        """
+        input = tool.input_str
+        name = tool.name
+        emoji = CHECKMARK_EMOJI if is_complete else THINKING_EMOJI
+        if name == "_Exception":
+            emoji = EXCEPTION_EMOJI
+            name = "Parsing error"
+        idx = min([60, len(input)])
+        input = input[0:idx]
+        if len(tool.input_str) > idx:
+            input = input + "..."
+        input = input.replace("\n", " ")
+        label = f"{emoji} **{name}:** {input}"
+        return label
+
+    @staticmethod
+    def get_history_label() -> str:
+        """Return a markdown label for the special 'history' container
+        that contains overflow thoughts.
+        """
+        return f"{HISTORY_EMOJI} **History**"
+
+    @staticmethod
+    def get_final_agent_thought_label() -> str:
+        """Return the markdown label for the agent's final thought -
+        the "Now I have the answer" thought, that doesn't involve
+        a tool.
+        """
+        return f"{CHECKMARK_EMOJI} **Complete!**"
+
+
+class LLMThought:
+    """A thought in the LLM's thought stream."""
+
+    def __init__(
+        self,
+        parent_container: DeltaGenerator,
+        labeler: LLMThoughtLabeler,
+        expanded: bool,
+        collapse_on_complete: bool,
+    ):
+        """Initialize the LLMThought.
+
+        Args:
+            parent_container: The container we're writing into.
+            labeler: The labeler to use for this thought.
+            expanded: Whether the thought should be expanded by default.
+            collapse_on_complete: Whether the thought should be collapsed.
+        """
+        self._container = MutableExpander(
+            parent_container=parent_container,
+            label=labeler.get_initial_label(),
+            expanded=expanded,
+        )
+        self._state = LLMThoughtState.THINKING
+        self._llm_token_stream = ""
+        self._llm_token_writer_idx: Optional[int] = None
+        self._last_tool: Optional[ToolRecord] = None
+        self._collapse_on_complete = collapse_on_complete
+        self._labeler = labeler
+
+    @property
+    def container(self) -> MutableExpander:
+        """The container we're writing into."""
+        return self._container
+
+    @property
+    def last_tool(self) -> Optional[ToolRecord]:
+        """The last tool executed by this thought"""
+        return self._last_tool
+
+    def _reset_llm_token_stream(self) -> None:
+        self._llm_token_stream = ""
+        self._llm_token_writer_idx = None
+
+    def on_llm_start(self, serialized: Dict[str, Any], prompts: List[str]) -> None:
+        self._reset_llm_token_stream()
+
+    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
+        # This is only called when the LLM is initialized with `streaming=True`
+        self._llm_token_stream += _convert_newlines(token)
+        self._llm_token_writer_idx = self._container.markdown(
+            self._llm_token_stream, index=self._llm_token_writer_idx
+        )
+
+    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
+        # `response` is the concatenation of all the tokens received by the LLM.
+        # If we're receiving streaming tokens from `on_llm_new_token`, this response
+        # data is redundant
+        self._reset_llm_token_stream()
+
+    def on_llm_error(self, error: BaseException, **kwargs: Any) -> None:
+        self._container.markdown("**LLM encountered an error...**")
+        self._container.exception(error)
+
+    def on_tool_start(
+        self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
+    ) -> None:
+        # Called with the name of the tool we're about to run (in `serialized[name]`),
+        # and its input. We change our container's label to be the tool name.
+        self._state = LLMThoughtState.RUNNING_TOOL
+        tool_name = serialized["name"]
+        self._last_tool = ToolRecord(name=tool_name, input_str=input_str)
+        self._container.update(
+            new_label=self._labeler.get_tool_label(self._last_tool, is_complete=False)
+        )
+
+    def on_tool_end(
+        self,
+        output: Any,
+        color: Optional[str] = None,
+        observation_prefix: Optional[str] = None,
+        llm_prefix: Optional[str] = None,
+        **kwargs: Any,
+    ) -> None:
+        self._container.markdown(f"**{str(output)}**")
+
+    def on_tool_error(self, error: BaseException, **kwargs: Any) -> None:
+        self._container.markdown("**Tool encountered an error...**")
+        self._container.exception(error)
+
+    def on_agent_action(
+        self, action: AgentAction, color: Optional[str] = None, **kwargs: Any
+    ) -> Any:
+        # Called when we're about to kick off a new tool. The `action` data
+        # tells us the tool we're about to use, and the input we'll give it.
+        # We don't output anything here, because we'll receive this same data
+        # when `on_tool_start` is called immediately after.
+        pass
+
+    def complete(self, final_label: Optional[str] = None) -> None:
+        """Finish the thought."""
+        if final_label is None and self._state == LLMThoughtState.RUNNING_TOOL:
+            assert self._last_tool is not None, (
+                "_last_tool should never be null when _state == RUNNING_TOOL"
+            )
+            final_label = self._labeler.get_tool_label(
+                self._last_tool, is_complete=True
+            )
+        self._state = LLMThoughtState.COMPLETE
+        if self._collapse_on_complete:
+            self._container.update(new_label=final_label, new_expanded=False)
+        else:
+            self._container.update(new_label=final_label)
+
+    def clear(self) -> None:
+        """Remove the thought from the screen. A cleared thought can't be reused."""
+        self._container.clear()
+
+
+class StreamlitCallbackHandler(BaseCallbackHandler):
+    """Callback handler that writes to a Streamlit app."""
+
+    def __init__(
+        self,
+        parent_container: DeltaGenerator,
+        *,
+        max_thought_containers: int = 4,
+        expand_new_thoughts: bool = True,
+        collapse_completed_thoughts: bool = True,
+        thought_labeler: Optional[LLMThoughtLabeler] = None,
+    ):
+        """Create a StreamlitCallbackHandler instance.
+
+        Parameters
+        ----------
+        parent_container
+            The `st.container` that will contain all the Streamlit elements that the
+            Handler creates.
+        max_thought_containers
+            The max number of completed LLM thought containers to show at once. When
+            this threshold is reached, a new thought will cause the oldest thoughts to
+            be collapsed into a "History" expander. Defaults to 4.
+        expand_new_thoughts
+            Each LLM "thought" gets its own `st.expander`. This param controls whether
+            that expander is expanded by default. Defaults to True.
+        collapse_completed_thoughts
+            If True, LLM thought expanders will be collapsed when completed.
+            Defaults to True.
+        thought_labeler
+            An optional custom LLMThoughtLabeler instance. If unspecified, the handler
+            will use the default thought labeling logic. Defaults to None.
+        """
+        self._parent_container = parent_container
+        self._history_parent = parent_container.container()
+        self._history_container: Optional[MutableExpander] = None
+        self._current_thought: Optional[LLMThought] = None
+        self._completed_thoughts: List[LLMThought] = []
+        self._max_thought_containers = max(max_thought_containers, 1)
+        self._expand_new_thoughts = expand_new_thoughts
+        self._collapse_completed_thoughts = collapse_completed_thoughts
+        self._thought_labeler = thought_labeler or LLMThoughtLabeler()
+
+    def _require_current_thought(self) -> LLMThought:
+        """Return our current LLMThought. Raise an error if we have no current
+        thought.
+        """
+        if self._current_thought is None:
+            raise RuntimeError("Current LLMThought is unexpectedly None!")
+        return self._current_thought
+
+    def _get_last_completed_thought(self) -> Optional[LLMThought]:
+        """Return our most recent completed LLMThought, or None if we don't have one."""
+        if len(self._completed_thoughts) > 0:
+            return self._completed_thoughts[len(self._completed_thoughts) - 1]
+        return None
+
+    @property
+    def _num_thought_containers(self) -> int:
+        """The number of 'thought containers' we're currently showing: the
+        number of completed thought containers, the history container (if it exists),
+        and the current thought container (if it exists).
+        """
+        count = len(self._completed_thoughts)
+        if self._history_container is not None:
+            count += 1
+        if self._current_thought is not None:
+            count += 1
+        return count
+
+    def _complete_current_thought(self, final_label: Optional[str] = None) -> None:
+        """Complete the current thought, optionally assigning it a new label.
+        Add it to our _completed_thoughts list.
+        """
+        thought = self._require_current_thought()
+        thought.complete(final_label)
+        self._completed_thoughts.append(thought)
+        self._current_thought = None
+
+    def _prune_old_thought_containers(self) -> None:
+        """If we have too many thoughts onscreen, move older thoughts to the
+        'history container.'
+        """
+        while (
+            self._num_thought_containers > self._max_thought_containers
+            and len(self._completed_thoughts) > 0
+        ):
+            # Create our history container if it doesn't exist, and if
+            # max_thought_containers is > 1. (if max_thought_containers is 1, we don't
+            # have room to show history.)
+            if self._history_container is None and self._max_thought_containers > 1:
+                self._history_container = MutableExpander(
+                    self._history_parent,
+                    label=self._thought_labeler.get_history_label(),
+                    expanded=False,
+                )
+
+            oldest_thought = self._completed_thoughts.pop(0)
+            if self._history_container is not None:
+                self._history_container.markdown(oldest_thought.container.label)
+                self._history_container.append_copy(oldest_thought.container)
+            oldest_thought.clear()
+
+    def on_llm_start(
+        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
+    ) -> None:
+        if self._current_thought is None:
+            self._current_thought = LLMThought(
+                parent_container=self._parent_container,
+                expanded=self._expand_new_thoughts,
+                collapse_on_complete=self._collapse_completed_thoughts,
+                labeler=self._thought_labeler,
+            )
+
+        self._current_thought.on_llm_start(serialized, prompts)
+
+        # We don't prune_old_thought_containers here, because our container won't
+        # be visible until it has a child.
+
+    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
+        self._require_current_thought().on_llm_new_token(token, **kwargs)
+        self._prune_old_thought_containers()
+
+    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
+        self._require_current_thought().on_llm_end(response, **kwargs)
+        self._prune_old_thought_containers()
+
+    def on_llm_error(self, error: BaseException, **kwargs: Any) -> None:
+        self._require_current_thought().on_llm_error(error, **kwargs)
+        self._prune_old_thought_containers()
+
+    def on_tool_start(
+        self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
+    ) -> None:
+        self._require_current_thought().on_tool_start(serialized, input_str, **kwargs)
+        self._prune_old_thought_containers()
+
+    def on_tool_end(
+        self,
+        output: Any,
+        color: Optional[str] = None,
+        observation_prefix: Optional[str] = None,
+        llm_prefix: Optional[str] = None,
+        **kwargs: Any,
+    ) -> None:
+        output = str(output)
+        self._require_current_thought().on_tool_end(
+            output, color, observation_prefix, llm_prefix, **kwargs
+        )
+        self._complete_current_thought()
+
+    def on_tool_error(self, error: BaseException, **kwargs: Any) -> None:
+        self._require_current_thought().on_tool_error(error, **kwargs)
+        self._prune_old_thought_containers()
+
+    def on_text(
+        self,
+        text: str,
+        color: Optional[str] = None,
+        end: str = "",
+        **kwargs: Any,
+    ) -> None:
+        pass
+
+    def on_chain_start(
+        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
+    ) -> None:
+        pass
+
+    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
+        pass
+
+    def on_chain_error(self, error: BaseException, **kwargs: Any) -> None:
+        pass
+
+    def on_agent_action(
+        self, action: AgentAction, color: Optional[str] = None, **kwargs: Any
+    ) -> Any:
+        self._require_current_thought().on_agent_action(action, color, **kwargs)
+        self._prune_old_thought_containers()
+
+    def on_agent_finish(
+        self, finish: AgentFinish, color: Optional[str] = None, **kwargs: Any
+    ) -> None:
+        if self._current_thought is not None:
+            self._current_thought.complete(
+                self._thought_labeler.get_final_agent_thought_label()
+            )
+            self._current_thought = None