Allow passing custom prompts to GraphIndexCreator (#7381)

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Co-authored-by: Bagatur <baskaryan@gmail.com>

langchain/indexes/graph.py

@@ -3,6 +3,7 @@ from typing import Optional, Type
 
 from pydantic import BaseModel
 
+from langchain import BasePromptTemplate
 from langchain.chains.llm import LLMChain
 from langchain.graphs.networkx_graph import NetworkxEntityGraph, parse_triples
 from langchain.indexes.prompts.knowledge_triplet_extraction import (
@@ -17,24 +18,28 @@ class GraphIndexCreator(BaseModel):
     llm: Optional[BaseLanguageModel] = None
     graph_type: Type[NetworkxEntityGraph] = NetworkxEntityGraph
 
-    def from_text(self, text: str) -> NetworkxEntityGraph:
+    def from_text(
+        self, text: str, prompt: BasePromptTemplate = KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT
+    ) -> NetworkxEntityGraph:
         """Create graph index from text."""
         if self.llm is None:
             raise ValueError("llm should not be None")
         graph = self.graph_type()
-        chain = LLMChain(llm=self.llm, prompt=KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT)
+        chain = LLMChain(llm=self.llm, prompt=prompt)
         output = chain.predict(text=text)
         knowledge = parse_triples(output)
         for triple in knowledge:
             graph.add_triple(triple)
         return graph
 
-    async def afrom_text(self, text: str) -> NetworkxEntityGraph:
+    async def afrom_text(
+        self, text: str, prompt: BasePromptTemplate = KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT
+    ) -> NetworkxEntityGraph:
         """Create graph index from text asynchronously."""
         if self.llm is None:
             raise ValueError("llm should not be None")
         graph = self.graph_type()
-        chain = LLMChain(llm=self.llm, prompt=KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT)
+        chain = LLMChain(llm=self.llm, prompt=prompt)
         output = await chain.apredict(text=text)
         knowledge = parse_triples(output)
         for triple in knowledge: