github/langchain
1
0
Fork 0
mirror of https://github.com/hwchase17/langchain.git synced 2025-06-19 05:13:46 +00:00
Code Issues Packages Projects Releases Wiki Activity

experimental: Add config to convert_to_graph_documents (#24012)

Browse Source 
PR title: Experimental: Add config to convert_to_graph_documents

Description: In order to use langfuse, i need to pass the langfuse
configuration when invoking the chain. langchain_experimental does not
allow to add any parameters (beside the documents) to the
convert_to_graph_documents method. This way, I cannot monitor the chain
in langfuse.

If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.

---------

Co-authored-by: Catarina Franco <catarina.franco@criticalsoftware.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
This commit is contained in:
cffranco94 2024-07-30 18:01:06 +01:00 committed by GitHub
parent f2d810b3c0
commit b01d938997
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 13 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

21
libs/experimental/langchain_experimental/graph_transformers/llm.py
View File

@ -13,6 +13,7 @@ from langchain_core.prompts import (
PromptTemplate,
)
from langchain_core.pydantic_v1 import BaseModel, Field, create_model
from langchain_core.runnables import RunnableConfig
examples = [
{
@ -710,13 +711,15 @@ class LLMGraphTransformer:
prompt = prompt or default_prompt
self.chain = prompt | structured_llm
def process_response(self, document: Document) -> GraphDocument:
def process_response(
self, document: Document, config: Optional[RunnableConfig] = None
) -> 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 = self.chain.invoke({"input": text}, config=config)
if self._function_call:
raw_schema = cast(Dict[Any, Any], raw_schema)
nodes, relationships = _convert_to_graph_document(raw_schema)
@ -765,7 +768,7 @@ class LLMGraphTransformer:
return GraphDocument(nodes=nodes, relationships=relationships, source=document)
def convert_to_graph_documents(
self, documents: Sequence[Document]
self, documents: Sequence[Document], config: Optional[RunnableConfig] = None
) -> List[GraphDocument]:
"""Convert a sequence of documents into graph documents.
@ -776,15 +779,17 @@ class LLMGraphTransformer:
Returns:
Sequence[GraphDocument]: The transformed documents as graphs.
"""
return [self.process_response(document) for document in documents]
return [self.process_response(document, config) for document in documents]
async def aprocess_response(self, document: Document) -> GraphDocument:
async def aprocess_response(
self, document: Document, config: Optional[RunnableConfig] = None
) -> 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 = await self.chain.ainvoke({"input": text}, config=config)
raw_schema = cast(Dict[Any, Any], raw_schema)
nodes, relationships = _convert_to_graph_document(raw_schema)
@ -811,13 +816,13 @@ class LLMGraphTransformer:
return GraphDocument(nodes=nodes, relationships=relationships, source=document)
async def aconvert_to_graph_documents(
self, documents: Sequence[Document]
self, documents: Sequence[Document], config: Optional[RunnableConfig] = None
) -> List[GraphDocument]:
"""
Asynchronously convert a sequence of documents into graph documents.
"""
tasks = [
asyncio.create_task(self.aprocess_response(document))
asyncio.create_task(self.aprocess_response(document, config))
for document in documents
]
results = await asyncio.gather(*tasks)