feat: added Bedrock and Mistral to exec info

scrapegraphai/graphs/base_graph.py

@@ -5,7 +5,7 @@
 import warnings
 from typing import Tuple
 from ..telemetry import log_graph_execution
-from ..utils import CustomOpenAiCallbackManager
+from ..utils import CustomLLMCallbackManager
 
 class BaseGraph:
     """
@@ -52,7 +52,7 @@ def __init__(self, nodes: list, edges: list, entry_point: str, use_burr: bool =
         self.entry_point = entry_point.node_name
         self.graph_name = graph_name
         self.initial_state = {}
-        self.callback_manager = CustomOpenAiCallbackManager()
+        self.callback_manager = CustomLLMCallbackManager()
 
         if nodes[0].node_name != entry_point.node_name:
             # raise a warning if the entry point is not the first node in the list
@@ -108,6 +108,7 @@ def _execute_standard(self, initial_state: dict) -> Tuple[dict, list]:
         error_node = None
         source_type = None
         llm_model = None
+        llm_model_name = None
         embedder_model = None
         source = []
         prompt = None
@@ -135,9 +136,11 @@ def _execute_standard(self, initial_state: dict) -> Tuple[dict, list]:
             if hasattr(current_node, "llm_model") and llm_model is None:
                 llm_model = current_node.llm_model
                 if hasattr(llm_model, "model_name"):
-                    llm_model = llm_model.model_name
+                    llm_model_name = llm_model.model_name
                 elif hasattr(llm_model, "model"):
-                    llm_model = llm_model.model
+                    llm_model_name = llm_model.model
+                elif hasattr(llm_model, "model_id"):
+                    llm_model_name = llm_model.model_id
 
             if hasattr(current_node, "embedder_model") and embedder_model is None:
                 embedder_model = current_node.embedder_model
@@ -155,7 +158,7 @@ def _execute_standard(self, initial_state: dict) -> Tuple[dict, list]:
                             except Exception as e:
                                 schema = None
 
-            with self.callback_manager.exclusive_get_openai_callback() as cb:
+            with self.callback_manager.exclusive_get_callback(llm_model, llm_model_name) as cb:
                 try:
                     result = current_node.execute(state)
                 except Exception as e:
@@ -166,7 +169,7 @@ def _execute_standard(self, initial_state: dict) -> Tuple[dict, list]:
                         source=source,
                         prompt=prompt,
                         schema=schema,
-                        llm_model=llm_model,
+                        llm_model=llm_model_name,
                         embedder_model=embedder_model,
                         source_type=source_type,
                         execution_time=graph_execution_time,
@@ -222,7 +225,7 @@ def _execute_standard(self, initial_state: dict) -> Tuple[dict, list]:
             source=source,
             prompt=prompt,
             schema=schema,
-            llm_model=llm_model,
+            llm_model=llm_model_name,
             embedder_model=embedder_model,
             source_type=source_type,
             content=content,

scrapegraphai/utils/__init__.py

@@ -17,4 +17,4 @@
 from .screenshot_scraping.text_detection import detect_text
 from .tokenizer import num_tokens_calculus
 from .split_text_into_chunks import split_text_into_chunks
-from .custom_openai_callback import CustomOpenAiCallbackManager
+from .llm_callback_manager import CustomLLMCallbackManager

scrapegraphai/utils/custom_callback.py

@@ -0,0 +1,157 @@
+"""
+Custom callback for LLM token usage statistics.
+
+This module has been taken and modified from the OpenAI callback manager in langchian-community.
+https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/callbacks/openai_info.py
+"""
+from contextlib import contextmanager
+import threading
+from typing import Any, Dict, List, Optional
+from contextvars import ContextVar
+
+from langchain_core.callbacks import BaseCallbackHandler
+from langchain_core.messages import AIMessage
+from langchain_core.outputs import ChatGeneration, LLMResult
+from langchain_core.tracers.context import register_configure_hook
+
+from .model_costs import MODEL_COST_PER_1K_TOKENS_INPUT, MODEL_COST_PER_1K_TOKENS_OUTPUT
+
+
+def get_token_cost_for_model(
+    model_name: str, num_tokens: int, is_completion: bool = False
+) -> float:
+    """
+    Get the cost in USD for a given model and number of tokens.
+
+    Args:
+        model_name: Name of the model
+        num_tokens: Number of tokens.
+        is_completion: Whether the model is used for completion or not.
+            Defaults to False.
+
+    Returns:
+        Cost in USD.
+    """
+    if model_name not in MODEL_COST_PER_1K_TOKENS_INPUT:
+        return 0.0
+    if is_completion:
+        return MODEL_COST_PER_1K_TOKENS_OUTPUT[model_name] * (num_tokens / 1000)
+
+    return MODEL_COST_PER_1K_TOKENS_INPUT[model_name] * (num_tokens / 1000)
+
+
+class CustomCallbackHandler(BaseCallbackHandler):
+    """Callback Handler that tracks LLMs info."""
+
+    total_tokens: int = 0
+    prompt_tokens: int = 0
+    completion_tokens: int = 0
+    successful_requests: int = 0
+    total_cost: float = 0.0
+
+    def __init__(self, llm_model_name: str) -> None:
+        super().__init__()
+        self._lock = threading.Lock()
+        self.model_name = llm_model_name if llm_model_name else "unknown"
+
+    def __repr__(self) -> str:
+        return (
+            f"Tokens Used: {self.total_tokens}\n"
+            f"\tPrompt Tokens: {self.prompt_tokens}\n"
+            f"\tCompletion Tokens: {self.completion_tokens}\n"
+            f"Successful Requests: {self.successful_requests}\n"
+            f"Total Cost (USD): ${self.total_cost}"
+        )
+
+    @property
+    def always_verbose(self) -> bool:
+        """Whether to call verbose callbacks even if verbose is False."""
+        return True
+
+    def on_llm_start(
+        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
+    ) -> None:
+        """Print out the prompts."""
+        pass
+
+    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
+        """Print out the token."""
+        pass
+
+    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
+        """Collect token usage."""
+        # Check for usage_metadata (langchain-core >= 0.2.2)
+        try:
+            generation = response.generations[0][0]
+        except IndexError:
+            generation = None
+        if isinstance(generation, ChatGeneration):
+            try:
+                message = generation.message
+                if isinstance(message, AIMessage):
+                    usage_metadata = message.usage_metadata
+                else:
+                    usage_metadata = None
+            except AttributeError:
+                usage_metadata = None
+        else:
+            usage_metadata = None
+        if usage_metadata:
+            token_usage = {"total_tokens": usage_metadata["total_tokens"]}
+            completion_tokens = usage_metadata["output_tokens"]
+            prompt_tokens = usage_metadata["input_tokens"]
+
+
+        else:
+            if response.llm_output is None:
+                return None
+
+            if "token_usage" not in response.llm_output:
+                with self._lock:
+                    self.successful_requests += 1
+                return None
+
+            # compute tokens and cost for this request
+            token_usage = response.llm_output["token_usage"]
+            completion_tokens = token_usage.get("completion_tokens", 0)
+            prompt_tokens = token_usage.get("prompt_tokens", 0)
+        if self.model_name in MODEL_COST_PER_1K_TOKENS_INPUT:
+            completion_cost = get_token_cost_for_model(
+                self.model_name, completion_tokens, is_completion=True
+            )
+            prompt_cost = get_token_cost_for_model(self.model_name, prompt_tokens)
+        else:
+            completion_cost = 0
+            prompt_cost = 0
+
+        # update shared state behind lock
+        with self._lock:
+            self.total_cost += prompt_cost + completion_cost
+            self.total_tokens += token_usage.get("total_tokens", 0)
+            self.prompt_tokens += prompt_tokens
+            self.completion_tokens += completion_tokens
+            self.successful_requests += 1
+
+    def __copy__(self) -> "CustomCallbackHandler":
+        """Return a copy of the callback handler."""
+        return self
+
+    def __deepcopy__(self, memo: Any) -> "CustomCallbackHandler":
+        """Return a deep copy of the callback handler."""
+        return self
+
+
+custom_callback: ContextVar[Optional[CustomCallbackHandler]] = ContextVar(
+    "custom_callback", default=None
+)
+register_configure_hook(custom_callback, True)
+
+@contextmanager
+def get_custom_callback(llm_model_name: str):
+    """
+    Function to get custom callback for LLM token usage statistics.
+    """
+    cb = CustomCallbackHandler(llm_model_name)
+    custom_callback.set(cb)
+    yield cb
+    custom_callback.set(None)

scrapegraphai/utils/llm_callback_manager.py

@@ -0,0 +1,38 @@
+"""
+This module provides a custom callback manager for the LLM models.
+"""
+import threading
+from contextlib import contextmanager
+from .custom_callback import get_custom_callback
+
+from langchain_community.callbacks import get_openai_callback
+from langchain_community.callbacks.manager import get_bedrock_anthropic_callback
+from langchain_openai import ChatOpenAI, AzureChatOpenAI
+from langchain_aws import ChatBedrock
+
+class CustomLLMCallbackManager:
+    _lock = threading.Lock()
+
+    @contextmanager
+    def exclusive_get_callback(self, llm_model, llm_model_name):
+        if CustomLLMCallbackManager._lock.acquire(blocking=False):
+            if isinstance(llm_model, ChatOpenAI) or isinstance(llm_model, AzureChatOpenAI):
+                try:
+                    with get_openai_callback() as cb:
+                        yield cb
+                finally:
+                    CustomLLMCallbackManager._lock.release()
+            elif isinstance(llm_model, ChatBedrock) and llm_model_name is not None and "claude" in llm_model_name:
+                try:
+                    with get_bedrock_anthropic_callback() as cb:
+                        yield cb
+                finally:
+                    CustomLLMCallbackManager._lock.release()
+            else:
+                try:
+                    with get_custom_callback(llm_model_name) as cb:
+                        yield cb
+                finally:
+                    CustomLLMCallbackManager._lock.release()
+        else:
+            yield None

scrapegraphai/utils/model_costs.py

@@ -0,0 +1,105 @@
+"""
+This file contains the cost of models per 1k tokens for input and output.
+The file is on a best effort basis and may not be up to date. Any contributions are welcome.
+"""
+MODEL_COST_PER_1K_TOKENS_INPUT = {
+    ### MistralAI
+    # General Purpose
+    "open-mistral-nemo": 0.00015,
+    "open-mistral-nemo-2407": 0.00015,
+    "mistral-large": 0.002,
+    "mistral-large-2407": 0.002,
+    "mistral-small": 0.0002,
+    "mistral-small-2409": 0.0002,
+    # Specialist Models
+    "codestral": 0.0002,
+    "codestral-2405": 0.0002,
+    "pixtral-12b": 0.00015,
+    "pixtral-12b-2409": 0.00015,
+    # Legacy Models
+    "open-mistral-7b": 0.00025,
+    "open-mixtral-8x7b": 0.0007,
+    "open-mixtral-8x22b": 0.002,
+    "mistral-small-latest": 0.001,
+    "mistral-medium-latest": 0.00275,
+
+    ### Bedrock - not Claude
+    #AI21 Labs
+    "a121.ju-ultra-v1": 0.0188,
+    "a121.ju-mid-v1": 0.0125,
+    "ai21.jamba-instruct-v1:0": 0.0005,
+    # Meta - LLama
+    "meta.llama2-13b-chat-v1": 0.00075,
+    "meta.llama2-70b-chat-v1": 0.00195,
+    "meta.llama3-8b-instruct-v1:0": 0.0003,
+    "meta.llama3-70b-instruct-v1:0": 0.00265,
+    "meta.llama3-1-8b-instruct-v1:0": 0.00022,
+    "meta.llama3-1-70b-instruct-v1:0": 0.00099,
+    "meta.llama3-1-405b-instruct-v1:0": 0.00532,
+    # Cohere - Command
+    "cohere.command-text-v14": 0.0015,
+    "cohere.command-light-text-v14": 0.0003,
+    "cohere.command-r-v1:0": 0.0005,
+    "cohere.command-r-plus-v1:0": 0.003,
+    # Mistral
+    "mistral.mistral-7b-instruct-v0:2": 0.00015,
+    "mistral.mistral-large-2402-v1:0": 0.004,
+    "mistral.mistral-large-2407-v1:0": 0.002,
+    "mistral.mistral-small-2402-v1:0": 0.001,
+    "mistral.mixtral-7x8b-instruct-v0:1": 0.00045,
+    # Amazon - Titan
+    "amazon.titan-text-express-v1": 0.0002,
+    "amazon.titan-text-lite-v1": 0.00015,
+    "amazon.titan-text-premier-v1:0": 0.0005,
+}
+
+MODEL_COST_PER_1K_TOKENS_OUTPUT = {
+    ### MistralAI
+    # General Purpose
+    "open-mistral-nemo": 0.00015,
+    "open-mistral-nemo-2407": 0.00015,
+    "mistral-large": 0.002,
+    "mistral-large-2407": 0.006,
+    "mistral-small": 0.0002,
+    "mistral-small-2409": 0.0006,
+    # Specialist Models
+    "codestral": 0.0006,
+    "codestral-2405": 0.0006,
+    "pixtral-12b": 0.00015,
+    "pixtral-12b-2409": 0.0006,
+    # Legacy Models
+    "open-mistral-7b": 0.00025,
+    "open-mixtral-8x7b": 0.0007,
+    "open-mixtral-8x22b": 0.006,
+    "mistral-small-latest": 0.003,
+    "mistral-medium-latest": 0.0081,
+
+    ### Bedrock - not Claude
+    # AI21 Labs
+    "a121.ju-ultra-v1": 0.0188,
+    "a121.ju-mid-v1": 0.0125,
+    "ai21.jamba-instruct-v1:0": 0.0007,
+    # Meta - LLama
+    "meta.llama2-13b-chat-v1": 0.001,
+    "meta.llama2-70b-chat-v1": 0.00256,
+    "meta.llama3-8b-instruct-v1:0": 0.0006,
+    "meta.llama3-70b-instruct-v1:0": 0.0035,
+    "meta.llama3-1-8b-instruct-v1:0": 0.00022,
+    "meta.llama3-1-70b-instruct-v1:0": 0.00099,
+    "meta.llama3-1-405b-instruct-v1:0": 0.016,
+    # Cohere - Command
+    "cohere.command-text-v14": 0.002,
+    "cohere.command-light-text-v14": 0.0006,
+    "cohere.command-r-v1:0": 0.0015,
+    "cohere.command-r-plus-v1:0": 0.015,
+    # Mistral
+    "mistral.mistral-7b-instruct-v0:2": 0.0002,
+    "mistral.mistral-large-2402-v1:0": 0.012,
+    "mistral.mistral-large-2407-v1:0": 0.006,
+    "mistral.mistral-small-2402-v1:0": 0.003,
+    "mistral.mixtral-7x8b-instruct-v0:1": 0.0007,
+    # Amazon - Titan
+    "amazon.titan-text-express-v1": 0.0006,
+    "amazon.titan-text-lite-v1": 0.0002,
+    "amazon.titan-text-premier-v1:0": 0.0015,
+}