Harrison/official method (#1728)

Co-authored-by: Aratako <127325395+Aratako@users.noreply.github.com>

langchain/chat_models/openai.py

@@ -317,3 +317,41 @@ class ChatOpenAI(BaseChatModel, BaseModel):
 
         # calculate the number of tokens in the encoded text
         return len(tokenized_text)
+
+    def get_num_tokens_from_messages(
+        self, messages: List[BaseMessage], model: str = "gpt-3.5-turbo-0301"
+    ) -> int:
+        """Calculate num tokens for gpt-3.5-turbo with tiktoken package."""
+        try:
+            import tiktoken
+        except ImportError:
+            raise ValueError(
+                "Could not import tiktoken python package. "
+                "This is needed in order to calculate get_num_tokens. "
+                "Please it install it with `pip install tiktoken`."
+            )
+
+        """Returns the number of tokens used by a list of messages."""
+        try:
+            encoding = tiktoken.encoding_for_model(model)
+        except KeyError:
+            encoding = tiktoken.get_encoding("cl100k_base")
+        if model == "gpt-3.5-turbo-0301":  # note: future models may deviate from this
+            num_tokens = 0
+            messages_dict = [_convert_message_to_dict(m) for m in messages]
+            for message in messages_dict:
+                # every message follows <im_start>{role/name}\n{content}<im_end>\n
+                num_tokens += 4
+                for key, value in message.items():
+                    num_tokens += len(encoding.encode(value))
+                    if key == "name":  # if there's a name, the role is omitted
+                        num_tokens += -1  # role is always required and always 1 token
+            num_tokens += 2  # every reply is primed with <im_start>assistant
+            return num_tokens
+        else:
+            raise NotImplementedError(
+                f"get_num_tokens_from_messages() is not presently implemented "
+                f"for model {model}."
+                "See https://github.com/openai/openai-python/blob/main/chatml.md for "
+                "information on how messages are converted to tokens."
+            )

langchain/memory/buffer.py

@@ -3,7 +3,8 @@ from typing import Any, Dict, List, Optional
 from pydantic import BaseModel, root_validator
 
 from langchain.memory.chat_memory import BaseChatMemory, BaseMemory
-from langchain.memory.utils import get_buffer_string, get_prompt_input_key
+from langchain.memory.utils import get_prompt_input_key
+from langchain.schema import get_buffer_string
 
 
 class ConversationBufferMemory(BaseChatMemory, BaseModel):

langchain/memory/buffer_window.py

@@ -3,8 +3,7 @@ from typing import Any, Dict, List
 from pydantic import BaseModel
 
 from langchain.memory.chat_memory import BaseChatMemory
-from langchain.memory.utils import get_buffer_string
-from langchain.schema import BaseMessage
+from langchain.schema import BaseMessage, get_buffer_string
 
 
 class ConversationBufferWindowMemory(BaseChatMemory, BaseModel):

langchain/memory/entity.py

@@ -8,9 +8,9 @@ from langchain.memory.prompt import (
     ENTITY_EXTRACTION_PROMPT,
     ENTITY_SUMMARIZATION_PROMPT,
 )
-from langchain.memory.utils import get_buffer_string, get_prompt_input_key
+from langchain.memory.utils import get_prompt_input_key
 from langchain.prompts.base import BasePromptTemplate
-from langchain.schema import BaseLanguageModel, BaseMessage
+from langchain.schema import BaseLanguageModel, BaseMessage, get_buffer_string
 
 
 class ConversationEntityMemory(BaseChatMemory, BaseModel):

langchain/memory/kg.py

@@ -10,9 +10,9 @@ from langchain.memory.prompt import (
     ENTITY_EXTRACTION_PROMPT,
     KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT,
 )
-from langchain.memory.utils import get_buffer_string, get_prompt_input_key
+from langchain.memory.utils import get_prompt_input_key
 from langchain.prompts.base import BasePromptTemplate
-from langchain.schema import BaseLanguageModel, SystemMessage
+from langchain.schema import BaseLanguageModel, SystemMessage, get_buffer_string
 
 
 class ConversationKGMemory(BaseChatMemory, BaseModel):

langchain/memory/summary.py

@@ -5,9 +5,13 @@ from pydantic import BaseModel, root_validator
 from langchain.chains.llm import LLMChain
 from langchain.memory.chat_memory import BaseChatMemory
 from langchain.memory.prompt import SUMMARY_PROMPT
-from langchain.memory.utils import get_buffer_string
 from langchain.prompts.base import BasePromptTemplate
-from langchain.schema import BaseLanguageModel, BaseMessage, SystemMessage
+from langchain.schema import (
+    BaseLanguageModel,
+    BaseMessage,
+    SystemMessage,
+    get_buffer_string,
+)
 
 
 class SummarizerMixin(BaseModel):

langchain/memory/summary_buffer.py

@@ -4,8 +4,7 @@ from pydantic import BaseModel, root_validator
 
 from langchain.memory.chat_memory import BaseChatMemory
 from langchain.memory.summary import SummarizerMixin
-from langchain.memory.utils import get_buffer_string
-from langchain.schema import BaseMessage, SystemMessage
+from langchain.schema import BaseMessage, SystemMessage, get_buffer_string
 
 
 class ConversationSummaryBufferMemory(BaseChatMemory, SummarizerMixin, BaseModel):
@@ -55,21 +54,17 @@ class ConversationSummaryBufferMemory(BaseChatMemory, SummarizerMixin, BaseModel
             )
         return values
 
-    def get_num_tokens_list(self, arr: List[BaseMessage]) -> List[int]:
-        """Get list of number of tokens in each string in the input array."""
-        return [self.llm.get_num_tokens(get_buffer_string([x])) for x in arr]
-
     def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
         """Save context from this conversation to buffer."""
         super().save_context(inputs, outputs)
         # Prune buffer if it exceeds max token limit
         buffer = self.chat_memory.messages
-        curr_buffer_length = sum(self.get_num_tokens_list(buffer))
+        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 = sum(self.get_num_tokens_list(buffer))
+                curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
             self.moving_summary_buffer = self.predict_new_summary(
                 pruned_memory, self.moving_summary_buffer
             )

langchain/memory/utils.py

@@ -1,32 +1,6 @@
 from typing import Any, Dict, List
 
-from langchain.schema import (
-    AIMessage,
-    BaseMessage,
-    ChatMessage,
-    HumanMessage,
-    SystemMessage,
-)
-
-
-def get_buffer_string(
-    messages: List[BaseMessage], human_prefix: str = "Human", ai_prefix: str = "AI"
-) -> str:
-    """Get buffer string of messages."""
-    string_messages = []
-    for m in messages:
-        if isinstance(m, HumanMessage):
-            role = human_prefix
-        elif isinstance(m, AIMessage):
-            role = ai_prefix
-        elif isinstance(m, SystemMessage):
-            role = "System"
-        elif isinstance(m, ChatMessage):
-            role = m.role
-        else:
-            raise ValueError(f"Got unsupported message type: {m}")
-        string_messages.append(f"{role}: {m.content}")
-    return "\n".join(string_messages)
+from langchain.schema import get_buffer_string  # noqa: 401
 
 
 def get_prompt_input_key(inputs: Dict[str, Any], memory_variables: List[str]) -> str:

langchain/schema.py

@@ -7,6 +7,26 @@ from typing import Any, Dict, List, NamedTuple, Optional
 from pydantic import BaseModel, Extra, Field, root_validator
 
 
+def get_buffer_string(
+    messages: List[BaseMessage], human_prefix: str = "Human", ai_prefix: str = "AI"
+) -> str:
+    """Get buffer string of messages."""
+    string_messages = []
+    for m in messages:
+        if isinstance(m, HumanMessage):
+            role = human_prefix
+        elif isinstance(m, AIMessage):
+            role = ai_prefix
+        elif isinstance(m, SystemMessage):
+            role = "System"
+        elif isinstance(m, ChatMessage):
+            role = m.role
+        else:
+            raise ValueError(f"Got unsupported message type: {m}")
+        string_messages.append(f"{role}: {m.content}")
+    return "\n".join(string_messages)
+
+
 class AgentAction(NamedTuple):
     """Agent's action to take."""
 
@@ -185,6 +205,10 @@ class BaseLanguageModel(BaseModel, ABC):
         # calculate the number of tokens in the tokenized text
         return len(tokenized_text)
 
+    def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:
+        """Get the number of tokens in the message."""
+        return sum([self.get_num_tokens(get_buffer_string([m])) for m in messages])
+
 
 class BaseMemory(BaseModel, ABC):
     """Base interface for memory in chains."""