mirror of
https://github.com/hwchase17/langchain
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480626dc99
…tch]: import models from community ran ```bash git grep -l 'from langchain\.chat_models' | xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g" git grep -l 'from langchain\.llms' | xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g" git grep -l 'from langchain\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g" git checkout master libs/langchain/tests/unit_tests/llms git checkout master libs/langchain/tests/unit_tests/chat_models git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py make format cd libs/langchain; make format cd ../experimental; make format cd ../core; make format ```
84 lines
2.8 KiB
Python
84 lines
2.8 KiB
Python
"""Experimental implementation of lm-format-enforcer wrapped LLM."""
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from __future__ import annotations
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from typing import TYPE_CHECKING, Any, List, Optional
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from langchain.callbacks.manager import CallbackManagerForLLMRun
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from langchain.schema import LLMResult
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from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
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from langchain_experimental.pydantic_v1 import Field
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if TYPE_CHECKING:
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import lmformatenforcer
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def import_lmformatenforcer() -> lmformatenforcer:
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"""Lazily import lmformatenforcer."""
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try:
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import lmformatenforcer
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except ImportError:
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raise ImportError(
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"Could not import lmformatenforcer python package. "
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"Please install it with `pip install lm-format-enforcer`."
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)
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return lmformatenforcer
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class LMFormatEnforcer(HuggingFacePipeline):
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"""LMFormatEnforcer wrapped LLM using HuggingFace Pipeline API.
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This pipeline is experimental and not yet stable.
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"""
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json_schema: Optional[dict] = Field(
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description="The JSON Schema to complete.", default=None
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)
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regex: Optional[str] = Field(
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description="The regular expression to complete.", default=None
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)
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def _generate(
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self,
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prompts: List[str],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> LLMResult:
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lmformatenforcer = import_lmformatenforcer()
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import lmformatenforcer.integrations.transformers as hf_integration
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# We integrate lmformatenforcer by adding a prefix_allowed_tokens_fn.
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# It has to be done on each call, because the prefix function is stateful.
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if "prefix_allowed_tokens_fn" in self.pipeline._forward_params:
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raise ValueError(
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"prefix_allowed_tokens_fn param is forbidden with LMFormatEnforcer."
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)
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has_json_schema = self.json_schema is not None
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has_regex = self.regex is not None
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if has_json_schema == has_regex:
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raise ValueError(
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"You must specify exactly one of json_schema or a regex, but not both."
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)
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if has_json_schema:
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parser = lmformatenforcer.JsonSchemaParser(self.json_schema)
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else:
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parser = lmformatenforcer.RegexParser(self.regex)
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prefix_function = hf_integration.build_transformers_prefix_allowed_tokens_fn(
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self.pipeline.tokenizer, parser
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)
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self.pipeline._forward_params["prefix_allowed_tokens_fn"] = prefix_function
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result = super()._generate(
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prompts,
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stop=stop,
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run_manager=run_manager,
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**kwargs,
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)
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del self.pipeline._forward_params["prefix_allowed_tokens_fn"]
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return result
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