Delete Untitled.ipynb

libs/experimental/langchain_experimental/autonomous_agents/autogpt/memory.py

@@ -1,5 +1,6 @@
 from typing import Any, Dict, List
 
+from langchain.callbacks.manager import Callbacks
 from langchain.memory.chat_memory import BaseChatMemory, get_prompt_input_key
 from langchain.vectorstores.base import VectorStoreRetriever
 from pydantic import Field
@@ -19,10 +20,13 @@ class AutoGPTMemory(BaseChatMemory):
             return get_prompt_input_key(inputs, self.memory_variables)
         return self.input_key
 
-    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
+    def load_memory_variables(
+        self, inputs: Dict[str, Any], callbacks: Callbacks = None
+    ) -> Dict[str, Any]:
+        """Return key-value pairs given the text input to the chain."""
         input_key = self._get_prompt_input_key(inputs)
         query = inputs[input_key]
-        docs = self.retriever.get_relevant_documents(query)
+        docs = self.retriever.get_relevant_documents(query, callbacks=callbacks)
         return {
             "chat_history": self.chat_memory.messages[-10:],
             "relevant_context": docs,

libs/experimental/langchain_experimental/generative_agents/memory.py

@@ -3,6 +3,7 @@ import re
 from datetime import datetime
 from typing import Any, Dict, List, Optional
 
+from langchain.callbacks.manager import Callbacks
 from langchain.chains import LLMChain
 from langchain.prompts import PromptTemplate
 from langchain.retrievers import TimeWeightedVectorStoreRetriever
@@ -222,14 +223,21 @@ class GenerativeAgentMemory(BaseMemory):
         return result
 
     def fetch_memories(
-        self, observation: str, now: Optional[datetime] = None
+        self,
+        observation: str,
+        now: Optional[datetime] = None,
+        callbacks: Callbacks = None,
     ) -> List[Document]:
         """Fetch related memories."""
         if now is not None:
             with mock_now(now):
-                return self.memory_retriever.get_relevant_documents(observation)
+                return self.memory_retriever.get_relevant_documents(
+                    observation, callbacks=callbacks
+                )
         else:
-            return self.memory_retriever.get_relevant_documents(observation)
+            return self.memory_retriever.get_relevant_documents(
+                observation, callbacks=callbacks
+            )
 
     def format_memories_detail(self, relevant_memories: List[Document]) -> str:
         content = []
@@ -260,7 +268,9 @@ class GenerativeAgentMemory(BaseMemory):
         """Input keys this memory class will load dynamically."""
         return []
 
-    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
+    def load_memory_variables(
+        self, inputs: Dict[str, Any], callbacks: Callbacks = None
+    ) -> Dict[str, Any]:
         """Return key-value pairs given the text input to the chain."""
         queries = inputs.get(self.queries_key)
         now = inputs.get(self.now_key)

libs/langchain/langchain/agents/agent_iterator.py

@@ -95,7 +95,7 @@ class AgentExecutorIterator(BaseAgentExecutorIterator):
 
     @inputs.setter
     def inputs(self, inputs: Any) -> None:
-        self._inputs = self.agent_executor.prep_inputs(inputs)
+        self._inputs = self.agent_executor.prep_inputs(inputs, callbacks=None)
 
     @property
     def callbacks(self) -> Callbacks:

libs/langchain/langchain/chains/base.py

@@ -217,7 +217,6 @@ class Chain(Serializable, ABC):
             A dict of named outputs. Should contain all outputs specified in
                 `Chain.output_keys`.
         """
-        inputs = self.prep_inputs(inputs)
         callback_manager = CallbackManager.configure(
             callbacks,
             self.callbacks,
@@ -232,6 +231,7 @@ class Chain(Serializable, ABC):
             dumpd(self),
             inputs,
         )
+        inputs = self.prep_inputs(inputs, callbacks=run_manager.get_child())
         try:
             outputs = (
                 self._call(inputs, run_manager=run_manager)
@@ -284,7 +284,6 @@ class Chain(Serializable, ABC):
             A dict of named outputs. Should contain all outputs specified in
                 `Chain.output_keys`.
         """
-        inputs = self.prep_inputs(inputs)
         callback_manager = AsyncCallbackManager.configure(
             callbacks,
             self.callbacks,
@@ -299,6 +298,7 @@ class Chain(Serializable, ABC):
             dumpd(self),
             inputs,
         )
+        inputs = self.prep_inputs(inputs, callbacks=run_manager.get_child())
         try:
             outputs = (
                 await self._acall(inputs, run_manager=run_manager)
@@ -342,7 +342,9 @@ class Chain(Serializable, ABC):
         else:
             return {**inputs, **outputs}
 
-    def prep_inputs(self, inputs: Union[Dict[str, Any], Any]) -> Dict[str, str]:
+    def prep_inputs(
+        self, inputs: Union[Dict[str, Any], Any], *, callbacks: Callbacks
+    ) -> Dict[str, str]:
         """Validate and prepare chain inputs, including adding inputs from memory.
 
         Args:
@@ -369,7 +371,9 @@ class Chain(Serializable, ABC):
                 )
             inputs = {list(_input_keys)[0]: inputs}
         if self.memory is not None:
-            external_context = self.memory.load_memory_variables(inputs)
+            external_context = self.memory.load_memory_variables(
+                inputs, callbacks=callbacks
+            )
             inputs = dict(inputs, **external_context)
         self._validate_inputs(inputs)
         return inputs

libs/langchain/langchain/evaluation/agents/trajectory_eval_chain.py

@@ -262,11 +262,13 @@ The following is the expected answer. Use this to measure correctness:
             return ["score", "reasoning"]
         return ["score"]
 
-    def prep_inputs(self, inputs: Union[Dict[str, Any], Any]) -> Dict[str, str]:
+    def prep_inputs(
+        self, inputs: Union[Dict[str, Any], Any], *, callbacks: Callbacks
+    ) -> Dict[str, str]:
         """Validate and prep inputs."""
         if "reference" not in inputs:
             inputs["reference"] = self._format_reference(inputs.get("reference"))
-        return super().prep_inputs(inputs)
+        return super().prep_inputs(inputs, callbacks=callbacks)
 
     def _call(
         self,

libs/langchain/langchain/experimental/autonomous_agents/autogpt/memory.py

@@ -2,6 +2,7 @@ from typing import Any, Dict, List
 
 from pydantic import Field
 
+from langchain.callbacks.manager import Callbacks
 from langchain.memory.chat_memory import BaseChatMemory, get_prompt_input_key
 from langchain.vectorstores.base import VectorStoreRetriever
 
@@ -20,7 +21,9 @@ class AutoGPTMemory(BaseChatMemory):
             return get_prompt_input_key(inputs, self.memory_variables)
         return self.input_key
 
-    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
+    def load_memory_variables(
+        self, inputs: Dict[str, Any], callbacks: Callbacks = None
+    ) -> Dict[str, Any]:
         input_key = self._get_prompt_input_key(inputs)
         query = inputs[input_key]
         docs = self.retriever.get_relevant_documents(query)

libs/langchain/langchain/experimental/generative_agents/memory.py

@@ -4,6 +4,7 @@ from datetime import datetime
 from typing import Any, Dict, List, Optional
 
 from langchain import LLMChain
+from langchain.callbacks.manager import Callbacks
 from langchain.prompts import PromptTemplate
 from langchain.retrievers import TimeWeightedVectorStoreRetriever
 from langchain.schema import BaseMemory, Document
@@ -260,7 +261,9 @@ class GenerativeAgentMemory(BaseMemory):
         """Input keys this memory class will load dynamically."""
         return []
 
-    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
+    def load_memory_variables(
+        self, inputs: Dict[str, Any], callbacks: Callbacks = None
+    ) -> 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)

libs/langchain/langchain/memory/buffer.py

@@ -2,6 +2,7 @@ from typing import Any, Dict, List, Optional
 
 from pydantic import root_validator
 
+from langchain.callbacks.manager import Callbacks
 from langchain.memory.chat_memory import BaseChatMemory, BaseMemory
 from langchain.memory.utils import get_prompt_input_key
 from langchain.schema.messages import get_buffer_string
@@ -34,7 +35,9 @@ class ConversationBufferMemory(BaseChatMemory):
         """
         return [self.memory_key]
 
-    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
+    def load_memory_variables(
+        self, inputs: Dict[str, Any], callbacks: Callbacks = None
+    ) -> Dict[str, Any]:
         """Return history buffer."""
         return {self.memory_key: self.buffer}
 
@@ -66,7 +69,9 @@ class ConversationStringBufferMemory(BaseMemory):
         """
         return [self.memory_key]
 
-    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
+    def load_memory_variables(
+        self, inputs: Dict[str, Any], callbacks: Callbacks = None
+    ) -> Dict[str, str]:
         """Return history buffer."""
         return {self.memory_key: self.buffer}
 

libs/langchain/langchain/memory/buffer_window.py

@@ -1,5 +1,6 @@
 from typing import Any, Dict, List
 
+from langchain.callbacks.manager import Callbacks
 from langchain.memory.chat_memory import BaseChatMemory
 from langchain.schema.messages import BaseMessage, get_buffer_string
 
@@ -26,7 +27,9 @@ class ConversationBufferWindowMemory(BaseChatMemory):
         """
         return [self.memory_key]
 
-    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
+    def load_memory_variables(
+        self, inputs: Dict[str, Any], callbacks: Callbacks = None
+    ) -> Dict[str, str]:
         """Return history buffer."""
 
         buffer: Any = self.buffer[-self.k * 2 :] if self.k > 0 else []

libs/langchain/langchain/memory/combined.py

@@ -3,6 +3,7 @@ from typing import Any, Dict, List, Set
 
 from pydantic import validator
 
+from langchain.callbacks.manager import Callbacks
 from langchain.memory.chat_memory import BaseChatMemory
 from langchain.schema import BaseMemory
 
@@ -54,13 +55,15 @@ class CombinedMemory(BaseMemory):
 
         return memory_variables
 
-    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
+    def load_memory_variables(
+        self, inputs: Dict[str, Any], callbacks: Callbacks = None
+    ) -> Dict[str, str]:
         """Load all vars from sub-memories."""
         memory_data: Dict[str, Any] = {}
 
         # Collect vars from all sub-memories
         for memory in self.memories:
-            data = memory.load_memory_variables(inputs)
+            data = memory.load_memory_variables(inputs, callbacks=callbacks)
             memory_data = {
                 **memory_data,
                 **data,

libs/langchain/langchain/memory/entity.py

@@ -5,6 +5,7 @@ from typing import Any, Dict, Iterable, List, Optional
 
 from pydantic import BaseModel, Field
 
+from langchain.callbacks.manager import Callbacks
 from langchain.chains.llm import LLMChain
 from langchain.memory.chat_memory import BaseChatMemory
 from langchain.memory.prompt import (
@@ -285,7 +286,9 @@ class ConversationEntityMemory(BaseChatMemory):
         """
         return ["entities", self.chat_history_key]
 
-    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
+    def load_memory_variables(
+        self, inputs: Dict[str, Any], callbacks: Callbacks = None
+    ) -> Dict[str, Any]:
         """
         Returns chat history and all generated entities with summaries if available,
         and updates or clears the recent entity cache.

libs/langchain/langchain/memory/kg.py

@@ -2,6 +2,7 @@ from typing import Any, Dict, List, Type, Union
 
 from pydantic import Field
 
+from langchain.callbacks.manager import Callbacks
 from langchain.chains.llm import LLMChain
 from langchain.graphs import NetworkxEntityGraph
 from langchain.graphs.networkx_graph import KnowledgeTriple, get_entities, parse_triples
@@ -34,7 +35,9 @@ class ConversationKGMemory(BaseChatMemory):
     """Number of previous utterances to include in the context."""
     memory_key: str = "history"  #: :meta private:
 
-    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
+    def load_memory_variables(
+        self, inputs: Dict[str, Any], callbacks: Callbacks = None
+    ) -> Dict[str, Any]:
         """Return history buffer."""
         entities = self._get_current_entities(inputs)
 

libs/langchain/langchain/memory/motorhead_memory.py

@@ -2,6 +2,7 @@ from typing import Any, Dict, List, Optional
 
 import requests
 
+from langchain.callbacks.manager import Callbacks
 from langchain.memory.chat_memory import BaseChatMemory
 from langchain.schema.messages import get_buffer_string
 
@@ -64,7 +65,9 @@ class MotorheadMemory(BaseChatMemory):
         if context and context != "NONE":
             self.context = context
 
-    def load_memory_variables(self, values: Dict[str, Any]) -> Dict[str, Any]:
+    def load_memory_variables(
+        self, inputs: Dict[str, Any], callbacks: Callbacks = None
+    ) -> Dict[str, Any]:
         if self.return_messages:
             return {self.memory_key: self.chat_memory.messages}
         else:

libs/langchain/langchain/memory/readonly.py

@@ -1,5 +1,6 @@
 from typing import Any, Dict, List
 
+from langchain.callbacks.manager import Callbacks
 from langchain.schema import BaseMemory
 
 
@@ -13,9 +14,11 @@ class ReadOnlySharedMemory(BaseMemory):
         """Return memory variables."""
         return self.memory.memory_variables
 
-    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
+    def load_memory_variables(
+        self, inputs: Dict[str, Any], callbacks: Callbacks = None
+    ) -> Dict[str, str]:
         """Load memory variables from memory."""
-        return self.memory.load_memory_variables(inputs)
+        return self.memory.load_memory_variables(inputs, callbacks=callbacks)
 
     def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
         """Nothing should be saved or changed"""

libs/langchain/langchain/memory/simple.py

@@ -1,5 +1,6 @@
 from typing import Any, Dict, List
 
+from langchain.callbacks.manager import Callbacks
 from langchain.schema import BaseMemory
 
 
@@ -14,7 +15,9 @@ class SimpleMemory(BaseMemory):
     def memory_variables(self) -> List[str]:
         return list(self.memories.keys())
 
-    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
+    def load_memory_variables(
+        self, inputs: Dict[str, Any], callbacks: Callbacks = None
+    ) -> Dict[str, str]:
         return self.memories
 
     def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:

libs/langchain/langchain/memory/summary.py

@@ -4,6 +4,7 @@ from typing import Any, Dict, List, Type
 
 from pydantic import BaseModel, root_validator
 
+from langchain.callbacks.manager import Callbacks
 from langchain.chains.llm import LLMChain
 from langchain.memory.chat_memory import BaseChatMemory
 from langchain.memory.prompt import SUMMARY_PROMPT
@@ -67,7 +68,9 @@ class ConversationSummaryMemory(BaseChatMemory, SummarizerMixin):
         """
         return [self.memory_key]
 
-    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
+    def load_memory_variables(
+        self, inputs: Dict[str, Any], callbacks: Callbacks = None
+    ) -> Dict[str, Any]:
         """Return history buffer."""
         if self.return_messages:
             buffer: Any = [self.summary_message_cls(content=self.buffer)]

libs/langchain/langchain/memory/summary_buffer.py

@@ -2,6 +2,7 @@ from typing import Any, Dict, List
 
 from pydantic import root_validator
 
+from langchain.callbacks.manager import Callbacks
 from langchain.memory.chat_memory import BaseChatMemory
 from langchain.memory.summary import SummarizerMixin
 from langchain.schema.messages import BaseMessage, get_buffer_string
@@ -26,7 +27,9 @@ class ConversationSummaryBufferMemory(BaseChatMemory, SummarizerMixin):
         """
         return [self.memory_key]
 
-    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
+    def load_memory_variables(
+        self, inputs: Dict[str, Any], callbacks: Callbacks = None
+    ) -> Dict[str, Any]:
         """Return history buffer."""
         buffer = self.buffer
         if self.moving_summary_buffer != "":

libs/langchain/langchain/memory/token_buffer.py

@@ -1,5 +1,6 @@
 from typing import Any, Dict, List
 
+from langchain.callbacks.manager import Callbacks
 from langchain.memory.chat_memory import BaseChatMemory
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.messages import BaseMessage, get_buffer_string
@@ -27,7 +28,9 @@ class ConversationTokenBufferMemory(BaseChatMemory):
         """
         return [self.memory_key]
 
-    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
+    def load_memory_variables(
+        self, inputs: Dict[str, Any], callbacks: Callbacks = None
+    ) -> Dict[str, Any]:
         """Return history buffer."""
         buffer: Any = self.buffer
         if self.return_messages:

libs/langchain/langchain/memory/vectorstore.py

@@ -4,6 +4,7 @@ from typing import Any, Dict, List, Optional, Sequence, Union
 
 from pydantic import Field
 
+from langchain.callbacks.manager import Callbacks
 from langchain.memory.chat_memory import BaseMemory
 from langchain.memory.utils import get_prompt_input_key
 from langchain.schema import Document
@@ -40,12 +41,12 @@ class VectorStoreRetrieverMemory(BaseMemory):
         return self.input_key
 
     def load_memory_variables(
-        self, inputs: Dict[str, Any]
+        self, inputs: Dict[str, Any], callbacks: Callbacks = None
     ) -> Dict[str, Union[List[Document], str]]:
         """Return history buffer."""
         input_key = self._get_prompt_input_key(inputs)
         query = inputs[input_key]
-        docs = self.retriever.get_relevant_documents(query)
+        docs = self.retriever.get_relevant_documents(query, callbacks=callbacks)
         result: Union[List[Document], str]
         if not self.return_docs:
             result = "\n".join([doc.page_content for doc in docs])

libs/langchain/langchain/retrievers/self_query/base.py

@@ -94,7 +94,9 @@ class SelfQueryRetriever(BaseRetriever, BaseModel):
         Returns:
             List of relevant documents
         """
-        inputs = self.llm_chain.prep_inputs({"query": query})
+        inputs = self.llm_chain.prep_inputs(
+            {"query": query}, callbacks=run_manager.get_child()
+        )
         structured_query = cast(
             StructuredQuery,
             self.llm_chain.predict_and_parse(

libs/langchain/langchain/schema/memory.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 from abc import ABC, abstractmethod
 from typing import Any, Dict, List
 
+import langchain
 from langchain.load.serializable import Serializable
 from langchain.schema.messages import AIMessage, BaseMessage, HumanMessage
 
@@ -27,7 +28,7 @@ class BaseMemory(Serializable, ABC):
                 def memory_variables(self) -> List[str]:
                     return list(self.memories.keys())
 
-                def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
+                def load_memory_variables(self, inputs: Dict[str, Any], callbacks: Callbacks = None) -> Dict[str, Any]:
                     return self.memories
 
                 def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
@@ -48,7 +49,11 @@ class BaseMemory(Serializable, ABC):
         """The string keys this memory class will add to chain inputs."""
 
     @abstractmethod
-    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
+    def load_memory_variables(
+        self,
+        inputs: Dict[str, Any],
+        callbacks: "langchain.callbacks.manager.Callbacks" = None,
+    ) -> Dict[str, Any]:
         """Return key-value pairs given the text input to the chain."""
 
     @abstractmethod

libs/langchain/tests/unit_tests/chains/test_base.py

@@ -3,7 +3,7 @@ from typing import Any, Dict, List, Optional
 
 import pytest
 
-from langchain.callbacks.manager import CallbackManagerForChainRun
+from langchain.callbacks.manager import CallbackManagerForChainRun, Callbacks
 from langchain.chains.base import Chain
 from langchain.schema import RUN_KEY, BaseMemory
 from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
@@ -18,7 +18,7 @@ class FakeMemory(BaseMemory):
         return ["baz"]
 
     def load_memory_variables(
-        self, inputs: Optional[Dict[str, Any]] = None
+        self, inputs: Optional[Dict[str, Any]] = None, callbacks: Callbacks = None
     ) -> Dict[str, str]:
         """Return baz variable."""
         return {"baz": "foo"}