Describe the issue

When you use open source model, for generate prompt by autopromptemplate: python -m graphrag.prompt_tune --root /path/to/project --domain "environmental news" --method random --limit 10 --max-tokens 2048 --chunk-size 256 --no-entity-types --output /path/to/output , it leverages an issue cause by tiktoken, BPE tokenizer of Open AI model

Steps to reproduce

When you change settings yaml file , llm argument by something like llama 70 B:

encoding_model: cl100k_base
skip_workflows: []
llm:
  api_key: ${GROQ_API_KEY}
  type: openai_chat # or azure_openai_chat
  model: llama3-70b-8192
  model_supports_json: true # recommended if this is available for your model.
  max_tokens: 4000
  # request_timeout: 180.0
  api_base: https://api.groq.com/openai/v1
  # api_version: 2024-02-15-preview

Tokenizer error is leverage

GraphRAG Config Used


encoding_model: cl100k_base
skip_workflows: []
llm:
  api_key: ${GROQ_API_KEY}
  type: openai_chat # or azure_openai_chat
  model: llama3-70b-8192
  model_supports_json: true # recommended if this is available for your model.
  max_tokens: 4000
  # request_timeout: 180.0
  api_base: https://api.groq.com/openai/v1
  # api_version: 2024-02-15-preview
  # organization: <organization_id>
  # deployment_name: <azure_model_deployment_name>
  tokens_per_minute: 3000 # set a leaky bucket throttle
  requests_per_minute: 2 # set a leaky bucket throttle
  max_retries: 1
  # max_retry_wait: 10.0
  # sleep_on_rate_limit_recommendation: true # whether to sleep when azure suggests wait-times
  # concurrent_requests: 25 # the number of parallel inflight requests that may be made

parallelization:
  stagger: 0.3
  # num_threads: 50 # the number of threads to use for parallel processing

async_mode: threaded # or asyncio

embeddings:
  ## parallelization: override the global parallelization settings for embeddings
  async_mode: threaded # or asyncio
  llm:
    api_key: ${TOGETHER_API_KEY}
    type: openai_embedding # or azure_openai_embedding
    model: togethercomputer/m2-bert-80M-8k-retrieval
    api_base:  https://api.together.xyz/v1
    # api_version: 2024-02-15-preview
    # organization: <organization_id>
    # deployment_name: <azure_model_deployment_name>
    tokens_per_minute: 3000 # set a leaky bucket throttle
    requests_per_minute: 2 # set a leaky bucket throttle
    max_retries: 1
    # max_retry_wait: 10.0
    # sleep_on_rate_limit_recommendation: true # whether to sleep when azure suggests wait-times
    # concurrent_requests: 25 # the number of parallel inflight requests that may be made
    batch_size: 16 # the number of documents to send in a single request
    batch_max_tokens: 3000 # the maximum number of tokens to send in a single request
    # target: required # or optional

chunks:
  size: 300
  overlap: 100
  group_by_columns: [id] # by default, we don't allow chunks to cross documents

input:
  type: file # or blob
  file_type: text # or csv
  base_dir: "input"
  file_encoding: utf-8
  file_pattern: ".*\\.txt$"

cache:
  type: file # or blob
  base_dir: "cache"
  # connection_string: <azure_blob_storage_connection_string>
  # container_name: <azure_blob_storage_container_name>

storage:
  type: file # or blob
  base_dir: "output/${timestamp}/artifacts"
  # connection_string: <azure_blob_storage_connection_string>
  # container_name: <azure_blob_storage_container_name>

reporting:
  type: file # or console, blob
  base_dir: "output/${timestamp}/reports"
  # connection_string: <azure_blob_storage_connection_string>
  # container_name: <azure_blob_storage_container_name>

entity_extraction:
  ## llm: override the global llm settings for this task
  ## parallelization: override the global parallelization settings for this task
  ## async_mode: override the global async_mode settings for this task
  prompt: "prompts/entity_extraction.txt"
  entity_types: [organization,person,geo,event]
  max_gleanings: 0

summarize_descriptions:
  ## llm: override the global llm settings for this task
  ## parallelization: override the global parallelization settings for this task
  ## async_mode: override the global async_mode settings for this task
  prompt: "prompts/summarize_descriptions.txt"
  max_length: 500

claim_extraction:
  ## llm: override the global llm settings for this task
  ## parallelization: override the global parallelization settings for this task
  ## async_mode: override the global async_mode settings for this task
  # enabled: true
  prompt: "prompts/claim_extraction.txt"
  description: "Any claims or facts that could be relevant to information discovery."
  max_gleanings: 0

community_report:
  ## llm: override the global llm settings for this task
  ## parallelization: override the global parallelization settings for this task
  ## async_mode: override the global async_mode settings for this task
  prompt: "prompts/community_report.txt"
  max_length: 2000
  max_input_length: 8000

cluster_graph:
  max_cluster_size: 10

embed_graph:
  enabled: false # if true, will generate node2vec embeddings for nodes
  # num_walks: 10
  # walk_length: 40
  # window_size: 2
  # iterations: 3
  # random_seed: 597832

umap:
  enabled: false # if true, will generate UMAP embeddings for nodes

snapshots:
  graphml: false
  raw_entities: false
  top_level_nodes: false

local_search:
  # text_unit_prop: 0.5
  # community_prop: 0.1
  # conversation_history_max_turns: 5
  # top_k_mapped_entities: 10
  # top_k_relationships: 10
  # max_tokens: 12000

global_search:
  # max_tokens: 12000
  # data_max_tokens: 12000
  # map_max_tokens: 1000
  # reduce_max_tokens: 2000
  # concurrency: 32

Logs and screenshots

To solve i build a little bit adaptive classe , in utils/tokens.py, need to make model input as variable after or adjust it :

Licensed under the MIT License

"""Utilities for working with tokens."""

import tiktoken
from transformers import AutoTokenizer
from huggingface_hub import login

login(token = 'token')

``
class TokenizerWrapper:
    def __init__(self, model: str):
        self.model = model
        if model.startswith("gpt-") or model in ["cl100k_base"]:
            self.tokenizer = self._init_tiktoken(model)
        else:
            self.tokenizer = self._init_huggingface(model)

    def _init_tiktoken(self, model: str):
        try:
            return tiktoken.encoding_for_model(model)
        except KeyError:
            print(f"Warning: Model {model} not found. Using cl100k_base encoding.")
            return tiktoken.get_encoding("cl100k_base")

    def _init_huggingface(self, model: str):
        return AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-70B-Instruct")

    def encode(self, text: str) -> list:
        if isinstance(self.tokenizer, tiktoken.Encoding):
            return self.tokenizer.encode(text)
        else:  # HuggingFace tokenizer
            return self.tokenizer.encode(text, add_special_tokens=False)

    def decode(self, tokens: list) -> str:
        if isinstance(self.tokenizer, tiktoken.Encoding):
            return self.tokenizer.decode(tokens)
        else:  # HuggingFace tokenizer
            return self.tokenizer.decode(tokens)

def num_tokens_from_string(string: str, model: str) -> int:
    """Return the number of tokens in a text string."""
    tokenizer = TokenizerWrapper(model)
    return len(tokenizer.encode(string))

def string_from_tokens(tokens: list, model: str) -> str:
    """Return a text string from a list of tokens."""
    tokenizer = TokenizerWrapper(model)
    return tokenizer.decode(tokens)

Additional Information

GraphRAG Version:
Operating System:
Python Version:
Related Issues:

microsoft / graphrag

[Open source tokenizer support]: <title> #467