mirror of
https://github.com/hwchase17/langchain.git
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9ffca3b92a
Update imports to use core for the low-hanging fruit changes. Ran
following
```bash
git grep -l 'langchain.schema.runnable' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.runnable/langchain_core.runnables/g'
git grep -l 'langchain.schema.output_parser' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.output_parser/langchain_core.output_parsers/g'
git grep -l 'langchain.schema.messages' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.messages/langchain_core.messages/g'
git grep -l 'langchain.schema.chat_histry' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.chat_history/langchain_core.chat_history/g'
git grep -l 'langchain.schema.prompt_template' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.prompt_template/langchain_core.prompts/g'
git grep -l 'from langchain.pydantic_v1' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.pydantic_v1/from langchain_core.pydantic_v1/g'
git grep -l 'from langchain.tools.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.tools\.base/from langchain_core.tools/g'
git grep -l 'from langchain.chat_models.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.chat_models.base/from langchain_core.language_models.chat_models/g'
git grep -l 'from langchain.llms.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.llms\.base\ /from langchain_core.language_models.llms\ /g'
git grep -l 'from langchain.embeddings.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.embeddings\.base/from langchain_core.embeddings/g'
git grep -l 'from langchain.vectorstores.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.vectorstores\.base/from langchain_core.vectorstores/g'
git grep -l 'from langchain.agents.tools' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.agents\.tools/from langchain_core.tools/g'
git grep -l 'from langchain.schema.output' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.output\ /from langchain_core.outputs\ /g'
git grep -l 'from langchain.schema.embeddings' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.embeddings/from langchain_core.embeddings/g'
git grep -l 'from langchain.schema.document' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.document/from langchain_core.documents/g'
git grep -l 'from langchain.schema.agent' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.agent/from langchain_core.agents/g'
git grep -l 'from langchain.schema.prompt ' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.prompt\ /from langchain_core.prompt_values /g'
git grep -l 'from langchain.schema.language_model' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.language_model/from langchain_core.language_models/g'
```
73 lines
2.2 KiB
Python
73 lines
2.2 KiB
Python
from typing import List, Optional
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from langchain.graphs.graph_document import Node as BaseNode
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from langchain.graphs.graph_document import Relationship as BaseRelationship
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from langchain_core.pydantic_v1 import BaseModel, Field
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class Property(BaseModel):
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"""A single property consisting of key and value"""
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key: str = Field(..., description="key")
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value: str = Field(..., description="value")
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class Node(BaseNode):
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properties: Optional[List[Property]] = Field(
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None, description="List of node properties"
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)
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class Relationship(BaseRelationship):
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properties: Optional[List[Property]] = Field(
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None, description="List of relationship properties"
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)
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class KnowledgeGraph(BaseModel):
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"""Generate a knowledge graph with entities and relationships."""
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nodes: List[Node] = Field(..., description="List of nodes in the knowledge graph")
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rels: List[Relationship] = Field(
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..., description="List of relationships in the knowledge graph"
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)
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def format_property_key(s: str) -> str:
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words = s.split()
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if not words:
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return s
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first_word = words[0].lower()
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capitalized_words = [word.capitalize() for word in words[1:]]
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return "".join([first_word] + capitalized_words)
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def props_to_dict(props) -> dict:
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"""Convert properties to a dictionary."""
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properties = {}
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if not props:
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return properties
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for p in props:
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properties[format_property_key(p.key)] = p.value
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return properties
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def map_to_base_node(node: Node) -> BaseNode:
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"""Map the KnowledgeGraph Node to the base Node."""
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properties = props_to_dict(node.properties) if node.properties else {}
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# Add name property for better Cypher statement generation
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properties["name"] = node.id.title()
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return BaseNode(
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id=node.id.title(), type=node.type.capitalize(), properties=properties
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)
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def map_to_base_relationship(rel: Relationship) -> BaseRelationship:
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"""Map the KnowledgeGraph Relationship to the base Relationship."""
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source = map_to_base_node(rel.source)
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target = map_to_base_node(rel.target)
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properties = props_to_dict(rel.properties) if rel.properties else {}
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return BaseRelationship(
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source=source, target=target, type=rel.type, properties=properties
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)
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