Pydantic allows empty strings:
```
from langchain.pydantic_v1 import Field, BaseModel
class Property(BaseModel):
"""A single property consisting of key and value"""
key: str = Field(..., description="key")
value: str = Field(..., description="value")
x = Property(key="", value="")
```
Which can produce errors downstream. We simply ignore those records
- **Description:**
The generated relationships in the graph had no properties, but the
Relationship class was properly defined with properties. This made it
very difficult to transform conditional sentences into a graph. Adding
properties to relationships can solve this issue elegantly.
The changes expand on the existing LLMGraphTransformer implementation
but add the possibility to define allowed relationship properties like
this: LLMGraphTransformer(llm=llm, relationship_properties=["Condition",
"Time"],)
- **Issue:**
no issue found
- **Dependencies:**
n/a
- **Twitter handle:**
@IstvanSpace
-Quick Test
=================================================================
from dotenv import load_dotenv
import os
from langchain_community.graphs import Neo4jGraph
from langchain_experimental.graph_transformers import
LLMGraphTransformer
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.documents import Document
load_dotenv()
os.environ["NEO4J_URI"] = os.getenv("NEO4J_URI")
os.environ["NEO4J_USERNAME"] = os.getenv("NEO4J_USERNAME")
os.environ["NEO4J_PASSWORD"] = os.getenv("NEO4J_PASSWORD")
graph = Neo4jGraph()
llm = ChatOpenAI(temperature=0, model_name="gpt-4o")
llm_transformer = LLMGraphTransformer(llm=llm)
#text = "Harry potter likes pies, but only if it rains outside"
text = "Jack has a dog named Max. Jack only walks Max if it is sunny
outside."
documents = [Document(page_content=text)]
llm_transformer_props = LLMGraphTransformer(
llm=llm,
relationship_properties=["Condition"],
)
graph_documents_props =
llm_transformer_props.convert_to_graph_documents(documents)
print(f"Nodes:{graph_documents_props[0].nodes}")
print(f"Relationships:{graph_documents_props[0].relationships}")
graph.add_graph_documents(graph_documents_props)
---------
Co-authored-by: Istvan Lorincz <istvan.lorincz@pm.me>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
LLMs might sometimes return invalid response for LLM graph transformer.
Instead of failing due to pydantic validation, we skip it and manually
check and optionally fix error where we can, so that more information
gets extracted
**Description:**
While not technically incorrect, the TypeVar used for the `@beta`
decorator prevented pyright (and thus most vscode users) from correctly
seeing the types of functions/classes decorated with `@beta`.
This is in part due to a small bug in pyright
(https://github.com/microsoft/pyright/issues/7448 ) - however, the
`Type` bound in the typevar `C = TypeVar("C", Type, Callable)` is not
doing anything - classes are `Callables` by default, so by my
understanding binding to `Type` does not actually provide any more
safety - the modified annotation still works correctly for both
functions, properties, and classes.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- [x] **PR title**: "experimental: Enhance LLMGraphTransformer with
async processing and improved readability"
- [x] **PR message**:
- **Description:** This pull request refactors the `process_response`
and `convert_to_graph_documents` methods in the LLMGraphTransformer
class to improve code readability and adds async versions of these
methods for concurrent processing.
The main changes include:
- Simplifying list comprehensions and conditional logic in the
process_response method for better readability.
- Adding async versions aprocess_response and
aconvert_to_graph_documents to enable concurrent processing of
documents.
These enhancements aim to improve the overall efficiency and
maintainability of the `LLMGraphTransformer` class.
- **Issue:** N/A
- **Dependencies:** No additional dependencies required.
- **Twitter handle:** @jjovalle99
- [x] **Add tests and docs**: N/A (This PR does not introduce a new
integration)
- [x] **Lint and test**: Ran make format, make lint, and make test from
the root of the modified package(s). All tests pass successfully.
Additional notes:
- The changes made in this PR are backwards compatible and do not
introduce any breaking changes.
- The PR touches only the `LLMGraphTransformer` class within the
experimental package.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
