Add option to save/load graph cypher QA (#6219)

Similar as #5818

Added the functionality to save/load Graph Cypher QA Chain due to a user
reporting the following error

> raise NotImplementedError("Saving not supported for this chain
type.")\nNotImplementedError: Saving not supported for this chain
type.\n'

langchain/chains/graph_qa/cypher.py

@@ -59,6 +59,10 @@ def output_keys(self) -> List[str]:
         _output_keys = [self.output_key]
         return _output_keys
 
+    @property
+    def _chain_type(self) -> str:
+        return "graph_cypher_chain"
+
     @classmethod
     def from_llm(
         cls,

langchain/chains/loading.py

@@ -11,6 +11,7 @@
 from langchain.chains.combine_documents.map_rerank import MapRerankDocumentsChain
 from langchain.chains.combine_documents.refine import RefineDocumentsChain
 from langchain.chains.combine_documents.stuff import StuffDocumentsChain
+from langchain.chains.graph_qa.cypher import GraphCypherQAChain
 from langchain.chains.hyde.base import HypotheticalDocumentEmbedder
 from langchain.chains.llm import LLMChain
 from langchain.chains.llm_bash.base import LLMBashChain
@@ -416,6 +417,30 @@ def _load_vector_db_qa(config: dict, **kwargs: Any) -> VectorDBQA:
     )
 
 
+def _load_graph_cypher_chain(config: dict, **kwargs: Any) -> GraphCypherQAChain:
+    if "graph" in kwargs:
+        graph = kwargs.pop("graph")
+    else:
+        raise ValueError("`graph` must be present.")
+    if "cypher_generation_chain" in config:
+        cypher_generation_chain_config = config.pop("cypher_generation_chain")
+        cypher_generation_chain = load_chain_from_config(cypher_generation_chain_config)
+    else:
+        raise ValueError("`cypher_generation_chain` must be present.")
+    if "qa_chain" in config:
+        qa_chain_config = config.pop("qa_chain")
+        qa_chain = load_chain_from_config(qa_chain_config)
+    else:
+        raise ValueError("`qa_chain` must be present.")
+
+    return GraphCypherQAChain(
+        graph=graph,
+        cypher_generation_chain=cypher_generation_chain,
+        qa_chain=qa_chain,
+        **config,
+    )
+
+
 def _load_api_chain(config: dict, **kwargs: Any) -> APIChain:
     if "api_request_chain" in config:
         api_request_chain_config = config.pop("api_request_chain")
@@ -482,6 +507,7 @@ def _load_llm_requests_chain(config: dict, **kwargs: Any) -> LLMRequestsChain:
     "vector_db_qa_with_sources_chain": _load_vector_db_qa_with_sources_chain,
     "vector_db_qa": _load_vector_db_qa,
     "retrieval_qa": _load_retrieval_qa,
+    "graph_cypher_chain": _load_graph_cypher_chain,
 }
 
 

tests/integration_tests/chains/test_graph_database.py

@@ -2,6 +2,7 @@
 import os
 
 from langchain.chains.graph_qa.cypher import GraphCypherQAChain
+from langchain.chains.loading import load_chain
 from langchain.graphs import Neo4jGraph
 from langchain.llms.openai import OpenAI
 
@@ -168,3 +169,31 @@ def test_cypher_return_direct() -> None:
     output = chain.run("Who played in Pulp Fiction?")
     expected_output = [{"a.name": "Bruce Willis"}]
     assert output == expected_output
+
+
+def test_cypher_save_load() -> None:
+    """Test saving and loading."""
+
+    FILE_PATH = "cypher.yaml"
+
+    url = os.environ.get("NEO4J_URL")
+    username = os.environ.get("NEO4J_USERNAME")
+    password = os.environ.get("NEO4J_PASSWORD")
+    assert url is not None
+    assert username is not None
+    assert password is not None
+
+    graph = Neo4jGraph(
+        url=url,
+        username=username,
+        password=password,
+    )
+
+    chain = GraphCypherQAChain.from_llm(
+        OpenAI(temperature=0), graph=graph, return_direct=True
+    )
+
+    chain.save(file_path=FILE_PATH)
+    qa_loaded = load_chain(FILE_PATH, graph=graph)
+
+    assert qa_loaded == chain