Merge pull request #877 from ScrapeGraphAI/856-token-indices-sequence…

…-length

856 token indices sequence length

examples/local_models/smart_scraper_ollama.py

@@ -15,7 +15,7 @@
         "temperature": 0,
         "format": "json",  # Ollama needs the format to be specified explicitly
         # "base_url": "http://localhost:11434", # set ollama URL arbitrarily
-        "model_tokens": 1024,
+        "model_tokens": 4096,
     },
     "verbose": True,
     "headless": False,
@@ -25,7 +25,7 @@
 # Create the SmartScraperGraph instance and run it
 # ************************************************
 smart_scraper_graph = SmartScraperGraph(
-    prompt="Find some information about what does the company do, the name and a contact email.",
+    prompt="Find some information about what does the company do and the list of founders.",
     source="https://scrapegraphai.com/",
     config=graph_config,
 )

examples/local_models/smart_scraper_schema_ollama.py

@@ -1,31 +1,31 @@
-""" 
+"""
 Basic example of scraping pipeline using SmartScraper with schema
 """
+
 import json
-from typing import List
+
 from pydantic import BaseModel, Field
+
 from scrapegraphai.graphs import SmartScraperGraph
 from scrapegraphai.utils import prettify_exec_info
 
+
 # ************************************************
 # Define the configuration for the graph
 # ************************************************
 class Project(BaseModel):
     title: str = Field(description="The title of the project")
     description: str = Field(description="The description of the project")
 
+
 class Projects(BaseModel):
-    projects: List[Project]
+    projects: list[Project]
+
 
 graph_config = {
-    "llm": {
-        "model": "ollama/llama3.1",
-        "temperature": 0,
-        "format": "json",  # Ollama needs the format to be specified explicitly
-        # "base_url": "http://localhost:11434", # set ollama URL arbitrarily
-    },
+    "llm": {"model": "ollama/llama3.2", "temperature": 0, "model_tokens": 4096},
     "verbose": True,
-    "headless": False
+    "headless": False,
 }
 
 # ************************************************
@@ -36,8 +36,15 @@ class Projects(BaseModel):
     prompt="List me all the projects with their description",
     source="https://perinim.github.io/projects/",
     schema=Projects,
-    config=graph_config
+    config=graph_config,
 )
 
 result = smart_scraper_graph.run()
 print(json.dumps(result, indent=4))
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = smart_scraper_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))

pyproject.toml

@@ -30,8 +30,7 @@ dependencies = [
     "googlesearch-python>=1.2.5",
     "async-timeout>=4.0.3",
     "simpleeval>=1.0.0",
-    "jsonschema>=4.23.0",
-    "transformers>=4.46.3",
+    "jsonschema>=4.23.0"
 ]
 
 readme = "README.md"

scrapegraphai/docloaders/chromium.py

@@ -61,15 +61,15 @@ def __init__(
 
         dynamic_import(backend, message)
 
-        self.backend = backend
         self.browser_config = kwargs
         self.headless = headless
         self.proxy = parse_or_search_proxy(proxy) if proxy else None
         self.urls = urls
         self.load_state = load_state
         self.requires_js_support = requires_js_support
         self.storage_state = storage_state
-        self.browser_name = browser_name
+        self.backend = kwargs.get("backend", backend)
+        self.browser_name = kwargs.get("browser_name", browser_name)
         self.retry_limit = kwargs.get("retry_limit", retry_limit)
         self.timeout = kwargs.get("timeout", timeout)
 

scrapegraphai/graphs/abstract_graph.py

@@ -203,8 +203,9 @@ def _create_llm(self, llm_config: dict) -> object:
                 ]
             except KeyError:
                 print(
-                    f"""Model {llm_params['model_provider']}/{llm_params['model']} not found,
-                    using default token size (8192)"""
+                    f"""Max input tokens for model {llm_params['model_provider']}/{llm_params['model']} not found,
+                    please specify the model_tokens parameter in the llm section of the graph configuration.
+                    Using default token size: 8192"""
                 )
                 self.model_token = 8192
         else:

scrapegraphai/nodes/generate_answer_node.py

@@ -10,7 +10,7 @@
 from langchain_community.chat_models import ChatOllama
 from langchain_core.output_parsers import JsonOutputParser
 from langchain_core.runnables import RunnableParallel
-from langchain_openai import AzureChatOpenAI, ChatOpenAI
+from langchain_openai import ChatOpenAI
 from requests.exceptions import Timeout
 from tqdm import tqdm
 
@@ -59,7 +59,10 @@ def __init__(
         self.llm_model = node_config["llm_model"]
 
         if isinstance(node_config["llm_model"], ChatOllama):
-            self.llm_model.format = "json"
+            if node_config.get("schema", None) is None:
+                self.llm_model.format = "json"
+            else:
+                self.llm_model.format = self.node_config["schema"].model_json_schema()
 
         self.verbose = node_config.get("verbose", False)
         self.force = node_config.get("force", False)
@@ -123,8 +126,7 @@ def execute(self, state: dict) -> dict:
                 format_instructions = ""
 
         if (
-            isinstance(self.llm_model, (ChatOpenAI, AzureChatOpenAI))
-            and not self.script_creator
+            not self.script_creator
             or self.force
             and not self.script_creator
             or self.is_md_scraper

scrapegraphai/nodes/generate_answer_node_k_level.py

@@ -6,10 +6,11 @@
 
 from langchain.prompts import PromptTemplate
 from langchain_aws import ChatBedrock
+from langchain_community.chat_models import ChatOllama
 from langchain_core.output_parsers import JsonOutputParser
 from langchain_core.runnables import RunnableParallel
 from langchain_mistralai import ChatMistralAI
-from langchain_openai import AzureChatOpenAI, ChatOpenAI
+from langchain_openai import ChatOpenAI
 from tqdm import tqdm
 
 from ..prompts import (
@@ -55,6 +56,13 @@ def __init__(
         super().__init__(node_name, "node", input, output, 2, node_config)
 
         self.llm_model = node_config["llm_model"]
+
+        if isinstance(node_config["llm_model"], ChatOllama):
+            if node_config.get("schema", None) is None:
+                self.llm_model.format = "json"
+            else:
+                self.llm_model.format = self.node_config["schema"].model_json_schema()
+
         self.embedder_model = node_config.get("embedder_model", None)
         self.verbose = node_config.get("verbose", False)
         self.force = node_config.get("force", False)
@@ -92,8 +100,7 @@ def execute(self, state: dict) -> dict:
                 format_instructions = ""
 
         if (
-            isinstance(self.llm_model, (ChatOpenAI, AzureChatOpenAI))
-            and not self.script_creator
+            not self.script_creator
             or self.force
             and not self.script_creator
             or self.is_md_scraper

scrapegraphai/nodes/parse_node.py

@@ -96,7 +96,6 @@ def execute(self, state: dict) -> dict:
             chunks = split_text_into_chunks(
                 text=docs_transformed.page_content,
                 chunk_size=self.chunk_size - 250,
-                model=self.llm_model,
             )
         else:
             docs_transformed = docs_transformed[0]
@@ -115,11 +114,10 @@ def execute(self, state: dict) -> dict:
                 chunks = split_text_into_chunks(
                     text=docs_transformed.page_content,
                     chunk_size=chunk_size,
-                    model=self.llm_model,
                 )
             else:
                 chunks = split_text_into_chunks(
-                    text=docs_transformed, chunk_size=chunk_size, model=self.llm_model
+                    text=docs_transformed, chunk_size=chunk_size
                 )
 
         state.update({self.output[0]: chunks})

scrapegraphai/utils/split_text_into_chunks.py

@@ -4,14 +4,10 @@
 
 from typing import List
 
-from langchain_core.language_models.chat_models import BaseChatModel
-
 from .tokenizer import num_tokens_calculus
 
 
-def split_text_into_chunks(
-    text: str, chunk_size: int, model: BaseChatModel, use_semchunk=True
-) -> List[str]:
+def split_text_into_chunks(text: str, chunk_size: int, use_semchunk=True) -> List[str]:
     """
     Splits the text into chunks based on the number of tokens.
 
@@ -27,17 +23,17 @@ def split_text_into_chunks(
         from semchunk import chunk
 
         def count_tokens(text):
-            return num_tokens_calculus(text, model)
+            return num_tokens_calculus(text)
 
-        chunk_size = min(chunk_size - 500, int(chunk_size * 0.9))
+        chunk_size = min(chunk_size, int(chunk_size * 0.9))
 
         chunks = chunk(
             text=text, chunk_size=chunk_size, token_counter=count_tokens, memoize=False
         )
         return chunks
 
     else:
-        tokens = num_tokens_calculus(text, model)
+        tokens = num_tokens_calculus(text)
 
         if tokens <= chunk_size:
             return [text]
@@ -48,7 +44,7 @@ def count_tokens(text):
 
         words = text.split()
         for word in words:
-            word_tokens = num_tokens_calculus(word, model)
+            word_tokens = num_tokens_calculus(word)
             if current_length + word_tokens > chunk_size:
                 chunks.append(" ".join(current_chunk))
                 current_chunk = [word]

scrapegraphai/utils/tokenizer.py

@@ -2,35 +2,15 @@
 Module for counting tokens and splitting text into chunks
 """
 
-from langchain_core.language_models.chat_models import BaseChatModel
-from langchain_mistralai import ChatMistralAI
-from langchain_ollama import ChatOllama
-from langchain_openai import ChatOpenAI
+from .tokenizers.tokenizer_openai import num_tokens_openai
 
 
-def num_tokens_calculus(string: str, llm_model: BaseChatModel) -> int:
+def num_tokens_calculus(string: str) -> int:
     """
     Returns the number of tokens in a text string.
     """
-    if isinstance(llm_model, ChatOpenAI):
-        from .tokenizers.tokenizer_openai import num_tokens_openai
 
-        num_tokens_fn = num_tokens_openai
+    num_tokens_fn = num_tokens_openai
 
-    elif isinstance(llm_model, ChatMistralAI):
-        from .tokenizers.tokenizer_mistral import num_tokens_mistral
-
-        num_tokens_fn = num_tokens_mistral
-
-    elif isinstance(llm_model, ChatOllama):
-        from .tokenizers.tokenizer_ollama import num_tokens_ollama
-
-        num_tokens_fn = num_tokens_ollama
-
-    else:
-        from .tokenizers.tokenizer_openai import num_tokens_openai
-
-        num_tokens_fn = num_tokens_openai
-
-    num_tokens = num_tokens_fn(string, llm_model)
+    num_tokens = num_tokens_fn(string)
     return num_tokens

scrapegraphai/utils/tokenizers/tokenizer_openai.py

@@ -3,19 +3,17 @@
 """
 
 import tiktoken
-from langchain_core.language_models.chat_models import BaseChatModel
 
 from ..logging import get_logger
 
 
-def num_tokens_openai(text: str, llm_model: BaseChatModel) -> int:
+def num_tokens_openai(text: str) -> int:
     """
     Estimate the number of tokens in a given text using OpenAI's tokenization method,
     adjusted for different OpenAI models.
 
     Args:
         text (str): The text to be tokenized and counted.
-        llm_model (BaseChatModel): The specific OpenAI model to adjust tokenization.
 
     Returns:
         int: The number of tokens in the text.
@@ -25,7 +23,7 @@ def num_tokens_openai(text: str, llm_model: BaseChatModel) -> int:
 
     logger.debug(f"Counting tokens for text of {len(text)} characters")
 
-    encoding = tiktoken.encoding_for_model("gpt-4")
+    encoding = tiktoken.encoding_for_model("gpt-4o")
 
     num_tokens = len(encoding.encode(text))
     return num_tokens