mirror of https://github.com/hwchase17/langchain
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
428 lines
16 KiB
Python
428 lines
16 KiB
Python
import asyncio
|
|
import json
|
|
from typing import Any, Dict, List, Optional, Sequence, Tuple, Type, cast
|
|
|
|
from langchain_community.graphs.graph_document import GraphDocument, Node, Relationship
|
|
from langchain_core.documents import Document
|
|
from langchain_core.language_models import BaseLanguageModel
|
|
from langchain_core.prompts import ChatPromptTemplate
|
|
from langchain_core.pydantic_v1 import BaseModel, Field
|
|
|
|
system_prompt = (
|
|
"# Knowledge Graph Instructions for GPT-4\n"
|
|
"## 1. Overview\n"
|
|
"You are a top-tier algorithm designed for extracting information in structured "
|
|
"formats to build a knowledge graph.\n"
|
|
"Try to capture as much information from the text as possible without "
|
|
"sacrifing accuracy. Do not add any information that is not explicitly "
|
|
"mentioned in the text\n"
|
|
"- **Nodes** represent entities and concepts.\n"
|
|
"- The aim is to achieve simplicity and clarity in the knowledge graph, making it\n"
|
|
"accessible for a vast audience.\n"
|
|
"## 2. Labeling Nodes\n"
|
|
"- **Consistency**: Ensure you use available types for node labels.\n"
|
|
"Ensure you use basic or elementary types for node labels.\n"
|
|
"- For example, when you identify an entity representing a person, "
|
|
"always label it as **'person'**. Avoid using more specific terms "
|
|
"like 'mathematician' or 'scientist'"
|
|
" - **Node IDs**: Never utilize integers as node IDs. Node IDs should be "
|
|
"names or human-readable identifiers found in the text.\n"
|
|
"- **Relationships** represent connections between entities or concepts.\n"
|
|
"Ensure consistency and generality in relationship types when constructing "
|
|
"knowledge graphs. Instead of using specific and momentary types "
|
|
"such as 'BECAME_PROFESSOR', use more general and timeless relationship types "
|
|
"like 'PROFESSOR'. Make sure to use general and timeless relationship types!\n"
|
|
"## 3. Coreference Resolution\n"
|
|
"- **Maintain Entity Consistency**: When extracting entities, it's vital to "
|
|
"ensure consistency.\n"
|
|
'If an entity, such as "John Doe", is mentioned multiple times in the text '
|
|
'but is referred to by different names or pronouns (e.g., "Joe", "he"),'
|
|
"always use the most complete identifier for that entity throughout the "
|
|
'knowledge graph. In this example, use "John Doe" as the entity ID.\n'
|
|
"Remember, the knowledge graph should be coherent and easily understandable, "
|
|
"so maintaining consistency in entity references is crucial.\n"
|
|
"## 4. Strict Compliance\n"
|
|
"Adhere to the rules strictly. Non-compliance will result in termination."
|
|
)
|
|
|
|
default_prompt = ChatPromptTemplate.from_messages(
|
|
[
|
|
(
|
|
"system",
|
|
system_prompt,
|
|
),
|
|
(
|
|
"human",
|
|
(
|
|
"Tip: Make sure to answer in the correct format and do "
|
|
"not include any explanations. "
|
|
"Use the given format to extract information from the "
|
|
"following input: {input}"
|
|
),
|
|
),
|
|
]
|
|
)
|
|
|
|
|
|
def optional_enum_field(
|
|
enum_values: Optional[List[str]] = None,
|
|
description: str = "",
|
|
is_rel: bool = False,
|
|
**field_kwargs: Any,
|
|
) -> Any:
|
|
"""Utility function to conditionally create a field with an enum constraint."""
|
|
if enum_values:
|
|
return Field(
|
|
...,
|
|
enum=enum_values,
|
|
description=f"{description}. Available options are {enum_values}",
|
|
**field_kwargs,
|
|
)
|
|
else:
|
|
node_info = (
|
|
"Ensure you use basic or elementary types for node labels.\n"
|
|
"For example, when you identify an entity representing a person, "
|
|
"always label it as **'Person'**. Avoid using more specific terms "
|
|
"like 'Mathematician' or 'Scientist'"
|
|
)
|
|
rel_info = (
|
|
"Instead of using specific and momentary types such as "
|
|
"'BECAME_PROFESSOR', use more general and timeless relationship types like "
|
|
"'PROFESSOR'. However, do not sacrifice any accuracy for generality"
|
|
)
|
|
additional_info = rel_info if is_rel else node_info
|
|
return Field(..., description=description + additional_info, **field_kwargs)
|
|
|
|
|
|
class _Graph(BaseModel):
|
|
nodes: Optional[List]
|
|
relationships: Optional[List]
|
|
|
|
|
|
def create_simple_model(
|
|
node_labels: Optional[List[str]] = None, rel_types: Optional[List[str]] = None
|
|
) -> Type[_Graph]:
|
|
"""
|
|
Simple model allows to limit node and/or relationship types.
|
|
Doesn't have any node or relationship properties.
|
|
"""
|
|
|
|
class SimpleNode(BaseModel):
|
|
"""Represents a node in a graph with associated properties."""
|
|
|
|
id: str = Field(description="Name or human-readable unique identifier.")
|
|
type: str = optional_enum_field(
|
|
node_labels, description="The type or label of the node."
|
|
)
|
|
|
|
class SimpleRelationship(BaseModel):
|
|
"""Represents a directed relationship between two nodes in a graph."""
|
|
|
|
source_node_id: str = Field(
|
|
description="Name or human-readable unique identifier of source node"
|
|
)
|
|
source_node_type: str = optional_enum_field(
|
|
node_labels, description="The type or label of the source node."
|
|
)
|
|
target_node_id: str = Field(
|
|
description="Name or human-readable unique identifier of target node"
|
|
)
|
|
target_node_type: str = optional_enum_field(
|
|
node_labels, description="The type or label of the target node."
|
|
)
|
|
type: str = optional_enum_field(
|
|
rel_types, description="The type of the relationship.", is_rel=True
|
|
)
|
|
|
|
class DynamicGraph(_Graph):
|
|
"""Represents a graph document consisting of nodes and relationships."""
|
|
|
|
nodes: Optional[List[SimpleNode]] = Field(description="List of nodes")
|
|
relationships: Optional[List[SimpleRelationship]] = Field(
|
|
description="List of relationships"
|
|
)
|
|
|
|
return DynamicGraph
|
|
|
|
|
|
def map_to_base_node(node: Any) -> Node:
|
|
"""Map the SimpleNode to the base Node."""
|
|
return Node(id=node.id, type=node.type)
|
|
|
|
|
|
def map_to_base_relationship(rel: Any) -> Relationship:
|
|
"""Map the SimpleRelationship to the base Relationship."""
|
|
source = Node(id=rel.source_node_id, type=rel.source_node_type)
|
|
target = Node(id=rel.target_node_id, type=rel.target_node_type)
|
|
return Relationship(source=source, target=target, type=rel.type)
|
|
|
|
|
|
def _parse_and_clean_json(
|
|
argument_json: Dict[str, Any],
|
|
) -> Tuple[List[Node], List[Relationship]]:
|
|
nodes = []
|
|
for node in argument_json["nodes"]:
|
|
if not node.get("id"): # Id is mandatory, skip this node
|
|
continue
|
|
nodes.append(
|
|
Node(
|
|
id=node["id"],
|
|
type=node.get("type"),
|
|
)
|
|
)
|
|
relationships = []
|
|
for rel in argument_json["relationships"]:
|
|
# Mandatory props
|
|
if (
|
|
not rel.get("source_node_id")
|
|
or not rel.get("target_node_id")
|
|
or not rel.get("type")
|
|
):
|
|
continue
|
|
|
|
# Node type copying if needed from node list
|
|
if not rel.get("source_node_type"):
|
|
try:
|
|
rel["source_node_type"] = [
|
|
el.get("type")
|
|
for el in argument_json["nodes"]
|
|
if el["id"] == rel["source_node_id"]
|
|
][0]
|
|
except IndexError:
|
|
rel["source_node_type"] = None
|
|
if not rel.get("target_node_type"):
|
|
try:
|
|
rel["target_node_type"] = [
|
|
el.get("type")
|
|
for el in argument_json["nodes"]
|
|
if el["id"] == rel["target_node_id"]
|
|
][0]
|
|
except IndexError:
|
|
rel["target_node_type"] = None
|
|
|
|
source_node = Node(
|
|
id=rel["source_node_id"],
|
|
type=rel["source_node_type"],
|
|
)
|
|
target_node = Node(
|
|
id=rel["target_node_id"],
|
|
type=rel["target_node_type"],
|
|
)
|
|
relationships.append(
|
|
Relationship(
|
|
source=source_node,
|
|
target=target_node,
|
|
type=rel["type"],
|
|
)
|
|
)
|
|
return nodes, relationships
|
|
|
|
|
|
def _format_nodes(nodes: List[Node]) -> List[Node]:
|
|
return [
|
|
Node(
|
|
id=el.id.title() if isinstance(el.id, str) else el.id,
|
|
type=el.type.capitalize(),
|
|
)
|
|
for el in nodes
|
|
]
|
|
|
|
|
|
def _format_relationships(rels: List[Relationship]) -> List[Relationship]:
|
|
return [
|
|
Relationship(
|
|
source=_format_nodes([el.source])[0],
|
|
target=_format_nodes([el.target])[0],
|
|
type=el.type.replace(" ", "_").upper(),
|
|
)
|
|
for el in rels
|
|
]
|
|
|
|
|
|
def _convert_to_graph_document(
|
|
raw_schema: Dict[Any, Any],
|
|
) -> Tuple[List[Node], List[Relationship]]:
|
|
# If there are validation errors
|
|
if not raw_schema["parsed"]:
|
|
try:
|
|
try: # OpenAI type response
|
|
argument_json = json.loads(
|
|
raw_schema["raw"].additional_kwargs["tool_calls"][0]["function"][
|
|
"arguments"
|
|
]
|
|
)
|
|
except Exception: # Google type response
|
|
argument_json = json.loads(
|
|
raw_schema["raw"].additional_kwargs["function_call"]["arguments"]
|
|
)
|
|
|
|
nodes, relationships = _parse_and_clean_json(argument_json)
|
|
except Exception: # If we can't parse JSON
|
|
return ([], [])
|
|
else: # If there are no validation errors use parsed pydantic object
|
|
parsed_schema: _Graph = raw_schema["parsed"]
|
|
nodes = (
|
|
[map_to_base_node(node) for node in parsed_schema.nodes]
|
|
if parsed_schema.nodes
|
|
else []
|
|
)
|
|
|
|
relationships = (
|
|
[map_to_base_relationship(rel) for rel in parsed_schema.relationships]
|
|
if parsed_schema.relationships
|
|
else []
|
|
)
|
|
# Title / Capitalize
|
|
return _format_nodes(nodes), _format_relationships(relationships)
|
|
|
|
|
|
class LLMGraphTransformer:
|
|
"""Transform documents into graph-based documents using a LLM.
|
|
|
|
It allows specifying constraints on the types of nodes and relationships to include
|
|
in the output graph. The class doesn't support neither extract and node or
|
|
relationship properties
|
|
|
|
Args:
|
|
llm (BaseLanguageModel): An instance of a language model supporting structured
|
|
output.
|
|
allowed_nodes (List[str], optional): Specifies which node types are
|
|
allowed in the graph. Defaults to an empty list, allowing all node types.
|
|
allowed_relationships (List[str], optional): Specifies which relationship types
|
|
are allowed in the graph. Defaults to an empty list, allowing all relationship
|
|
types.
|
|
prompt (Optional[ChatPromptTemplate], optional): The prompt to pass to
|
|
the LLM with additional instructions.
|
|
strict_mode (bool, optional): Determines whether the transformer should apply
|
|
filtering to strictly adhere to `allowed_nodes` and `allowed_relationships`.
|
|
Defaults to True.
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
from langchain_experimental.graph_transformers import LLMGraphTransformer
|
|
from langchain_core.documents import Document
|
|
from langchain_openai import ChatOpenAI
|
|
|
|
llm=ChatOpenAI(temperature=0)
|
|
transformer = LLMGraphTransformer(
|
|
llm=llm,
|
|
allowed_nodes=["Person", "Organization"])
|
|
|
|
doc = Document(page_content="Elon Musk is suing OpenAI")
|
|
graph_documents = transformer.convert_to_graph_documents([doc])
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
llm: BaseLanguageModel,
|
|
allowed_nodes: List[str] = [],
|
|
allowed_relationships: List[str] = [],
|
|
prompt: ChatPromptTemplate = default_prompt,
|
|
strict_mode: bool = True,
|
|
) -> None:
|
|
if not hasattr(llm, "with_structured_output"):
|
|
raise ValueError(
|
|
"The specified LLM does not support the 'with_structured_output'. "
|
|
"Please ensure you are using an LLM that supports this feature."
|
|
)
|
|
self.allowed_nodes = allowed_nodes
|
|
self.allowed_relationships = allowed_relationships
|
|
self.strict_mode = strict_mode
|
|
|
|
# Define chain
|
|
schema = create_simple_model(allowed_nodes, allowed_relationships)
|
|
structured_llm = llm.with_structured_output(schema, include_raw=True)
|
|
self.chain = prompt | structured_llm
|
|
|
|
def process_response(self, document: Document) -> GraphDocument:
|
|
"""
|
|
Processes a single document, transforming it into a graph document using
|
|
an LLM based on the model's schema and constraints.
|
|
"""
|
|
text = document.page_content
|
|
raw_schema = self.chain.invoke({"input": text})
|
|
raw_schema = cast(Dict[Any, Any], raw_schema)
|
|
nodes, relationships = _convert_to_graph_document(raw_schema)
|
|
|
|
# Strict mode filtering
|
|
if self.strict_mode and (self.allowed_nodes or self.allowed_relationships):
|
|
if self.allowed_nodes:
|
|
lower_allowed_nodes = [el.lower() for el in self.allowed_nodes]
|
|
nodes = [
|
|
node for node in nodes if node.type.lower() in lower_allowed_nodes
|
|
]
|
|
relationships = [
|
|
rel
|
|
for rel in relationships
|
|
if rel.source.type.lower() in lower_allowed_nodes
|
|
and rel.target.type.lower() in lower_allowed_nodes
|
|
]
|
|
if self.allowed_relationships:
|
|
relationships = [
|
|
rel
|
|
for rel in relationships
|
|
if rel.type.lower()
|
|
in [el.lower() for el in self.allowed_relationships]
|
|
]
|
|
|
|
return GraphDocument(nodes=nodes, relationships=relationships, source=document)
|
|
|
|
def convert_to_graph_documents(
|
|
self, documents: Sequence[Document]
|
|
) -> List[GraphDocument]:
|
|
"""Convert a sequence of documents into graph documents.
|
|
|
|
Args:
|
|
documents (Sequence[Document]): The original documents.
|
|
**kwargs: Additional keyword arguments.
|
|
|
|
Returns:
|
|
Sequence[GraphDocument]: The transformed documents as graphs.
|
|
"""
|
|
return [self.process_response(document) for document in documents]
|
|
|
|
async def aprocess_response(self, document: Document) -> GraphDocument:
|
|
"""
|
|
Asynchronously processes a single document, transforming it into a
|
|
graph document.
|
|
"""
|
|
text = document.page_content
|
|
raw_schema = await self.chain.ainvoke({"input": text})
|
|
raw_schema = cast(Dict[Any, Any], raw_schema)
|
|
nodes, relationships = _convert_to_graph_document(raw_schema)
|
|
|
|
if self.strict_mode and (self.allowed_nodes or self.allowed_relationships):
|
|
if self.allowed_nodes:
|
|
lower_allowed_nodes = [el.lower() for el in self.allowed_nodes]
|
|
nodes = [
|
|
node for node in nodes if node.type.lower() in lower_allowed_nodes
|
|
]
|
|
relationships = [
|
|
rel
|
|
for rel in relationships
|
|
if rel.source.type.lower() in lower_allowed_nodes
|
|
and rel.target.type.lower() in lower_allowed_nodes
|
|
]
|
|
if self.allowed_relationships:
|
|
relationships = [
|
|
rel
|
|
for rel in relationships
|
|
if rel.type.lower()
|
|
in [el.lower() for el in self.allowed_relationships]
|
|
]
|
|
|
|
return GraphDocument(nodes=nodes, relationships=relationships, source=document)
|
|
|
|
async def aconvert_to_graph_documents(
|
|
self, documents: Sequence[Document]
|
|
) -> List[GraphDocument]:
|
|
"""
|
|
Asynchronously convert a sequence of documents into graph documents.
|
|
"""
|
|
tasks = [
|
|
asyncio.create_task(self.aprocess_response(document))
|
|
for document in documents
|
|
]
|
|
results = await asyncio.gather(*tasks)
|
|
return results
|