mirror of
https://github.com/hwchase17/langchain
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45 lines
1.8 KiB
Python
45 lines
1.8 KiB
Python
from typing import List, Optional
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from langchain_community.graphs import Neo4jGraph
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from langchain_core.documents import Document
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from langchain_experimental.graph_transformers import LLMGraphTransformer
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from langchain_openai import ChatOpenAI
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graph = Neo4jGraph()
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llm = ChatOpenAI(model="gpt-3.5-turbo-16k", temperature=0)
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def chain(
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text: str,
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allowed_nodes: Optional[List[str]] = None,
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allowed_relationships: Optional[List[str]] = None,
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) -> str:
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"""
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Process the given text to extract graph data and constructs a graph document from the extracted information.
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The constructed graph document is then added to the graph.
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Parameters:
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- text (str): The input text from which the information will be extracted to construct the graph.
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- allowed_nodes (Optional[List[str]]): A list of node labels to guide the extraction process.
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If not provided, extraction won't have specific restriction on node labels.
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- allowed_relationships (Optional[List[str]]): A list of relationship types to guide the extraction process.
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If not provided, extraction won't have specific restriction on relationship types.
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Returns:
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str: A confirmation message indicating the completion of the graph construction.
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""" # noqa: E501
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# Construct document based on text
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documents = [Document(page_content=text)]
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# Extract graph data using OpenAI functions
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llm_graph_transformer = LLMGraphTransformer(
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llm=llm,
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allowed_nodes=allowed_nodes,
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allowed_relationships=allowed_relationships,
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)
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graph_documents = llm_graph_transformer.convert_to_graph_documents(documents)
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# Store information into a graph
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graph.add_graph_documents(graph_documents)
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return "Graph construction finished"
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