mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
100 lines
3.9 KiB
Python
100 lines
3.9 KiB
Python
from typing import Optional, Type
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.language_models import BaseLanguageModel
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from langchain_core.prompts import BasePromptTemplate
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from langchain_core.prompts.prompt import PromptTemplate
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from langchain_community.graphs import NetworkxEntityGraph
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from langchain_community.graphs.networkx_graph import KG_TRIPLE_DELIMITER
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from langchain_community.graphs.networkx_graph import parse_triples
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# flake8: noqa
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_DEFAULT_KNOWLEDGE_TRIPLE_EXTRACTION_TEMPLATE = (
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"You are a networked intelligence helping a human track knowledge triples"
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" about all relevant people, things, concepts, etc. and integrating"
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" them with your knowledge stored within your weights"
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" as well as that stored in a knowledge graph."
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" Extract all of the knowledge triples from the text."
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" A knowledge triple is a clause that contains a subject, a predicate,"
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" and an object. The subject is the entity being described,"
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" the predicate is the property of the subject that is being"
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" described, and the object is the value of the property.\n\n"
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"EXAMPLE\n"
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"It's a state in the US. It's also the number 1 producer of gold in the US.\n\n"
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f"Output: (Nevada, is a, state){KG_TRIPLE_DELIMITER}(Nevada, is in, US)"
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f"{KG_TRIPLE_DELIMITER}(Nevada, is the number 1 producer of, gold)\n"
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"END OF EXAMPLE\n\n"
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"EXAMPLE\n"
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"I'm going to the store.\n\n"
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"Output: NONE\n"
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"END OF EXAMPLE\n\n"
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"EXAMPLE\n"
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"Oh huh. I know Descartes likes to drive antique scooters and play the mandolin.\n"
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f"Output: (Descartes, likes to drive, antique scooters){KG_TRIPLE_DELIMITER}(Descartes, plays, mandolin)\n"
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"END OF EXAMPLE\n\n"
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"EXAMPLE\n"
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"{text}"
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"Output:"
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)
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KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT = PromptTemplate(
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input_variables=["text"],
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template=_DEFAULT_KNOWLEDGE_TRIPLE_EXTRACTION_TEMPLATE,
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)
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class GraphIndexCreator(BaseModel):
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"""Functionality to create graph index."""
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llm: Optional[BaseLanguageModel] = None
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graph_type: Type[NetworkxEntityGraph] = NetworkxEntityGraph
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def from_text(
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self, text: str, prompt: BasePromptTemplate = KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT
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) -> NetworkxEntityGraph:
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"""Create graph index from text."""
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if self.llm is None:
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raise ValueError("llm should not be None")
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graph = self.graph_type()
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# Temporary local scoped import while community does not depend on
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# langchain explicitly
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try:
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from langchain.chains import LLMChain
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except ImportError:
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raise ImportError(
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"Please install langchain to use this functionality. "
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"You can install it with `pip install langchain`."
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)
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chain = LLMChain(llm=self.llm, prompt=prompt)
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output = chain.predict(text=text)
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knowledge = parse_triples(output)
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for triple in knowledge:
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graph.add_triple(triple)
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return graph
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async def afrom_text(
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self, text: str, prompt: BasePromptTemplate = KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT
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) -> NetworkxEntityGraph:
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"""Create graph index from text asynchronously."""
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if self.llm is None:
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raise ValueError("llm should not be None")
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graph = self.graph_type()
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# Temporary local scoped import while community does not depend on
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# langchain explicitly
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try:
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from langchain.chains import LLMChain
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except ImportError:
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raise ImportError(
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"Please install langchain to use this functionality. "
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"You can install it with `pip install langchain`."
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)
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chain = LLMChain(llm=self.llm, prompt=prompt)
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output = await chain.apredict(text=text)
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knowledge = parse_triples(output)
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for triple in knowledge:
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graph.add_triple(triple)
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return graph
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