Update neo4j cypher templates to the function callback (#20515)

Update Neo4j Cypher templates to use function callback to pass context
instead of passing it in user prompt.

Co-authored-by: Erick Friis <erick@langchain.dev>

templates/neo4j-cypher-memory/neo4j_cypher_memory/chain.py

@@ -1,13 +1,22 @@
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Union
 
 from langchain.chains.graph_qa.cypher_utils import CypherQueryCorrector, Schema
 from langchain.memory import ChatMessageHistory
-from langchain_community.chat_models import ChatOpenAI
 from langchain_community.graphs import Neo4jGraph
+from langchain_core.messages import (
+    AIMessage,
+    SystemMessage,
+    ToolMessage,
+)
 from langchain_core.output_parsers import StrOutputParser
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.prompts import (
+    ChatPromptTemplate,
+    HumanMessagePromptTemplate,
+    MessagesPlaceholder,
+)
 from langchain_core.pydantic_v1 import BaseModel
 from langchain_core.runnables import RunnablePassthrough
+from langchain_openai import ChatOpenAI
 
 # Connection to Neo4j
 graph = Neo4jGraph()
@@ -56,7 +65,9 @@ def get_history(input: Dict[str, Any]) -> ChatMessageHistory:
 
 
 def save_history(input):
-    input.pop("response")
+    print(input)
+    if input.get("function_response"):
+        input.pop("function_response")
     # store history to database
     graph.query(
         """MERGE (u:User {id: $user_id})
@@ -107,26 +118,51 @@ cypher_response = (
 )
 
 # Generate natural language response based on database results
-response_template = """Based on the the question, Cypher query, and Cypher response, write a natural language response:
-Question: {question}
-Cypher query: {query}
-Cypher Response: {response}"""  # noqa: E501
+response_system = """You are an assistant that helps to form nice and human 
+understandable answers based on the provided information from tools.
+Do not add any other information that wasn't present in the tools, and use 
+very concise style in interpreting results!
+"""
 
 response_prompt = ChatPromptTemplate.from_messages(
     [
-        (
-            "system",
-            "Given an input question and Cypher response, convert it to a "
-            "natural language answer. No pre-amble.",
-        ),
-        ("human", response_template),
+        SystemMessage(content=response_system),
+        HumanMessagePromptTemplate.from_template("{question}"),
+        MessagesPlaceholder(variable_name="function_response"),
     ]
 )
 
+
+def get_function_response(
+    query: str, question: str
+) -> List[Union[AIMessage, ToolMessage]]:
+    context = graph.query(cypher_validation(query))
+    TOOL_ID = "call_H7fABDuzEau48T10Qn0Lsh0D"
+    messages = [
+        AIMessage(
+            content="",
+            additional_kwargs={
+                "tool_calls": [
+                    {
+                        "id": TOOL_ID,
+                        "function": {
+                            "arguments": '{"question":"' + question + '"}',
+                            "name": "GetInformation",
+                        },
+                        "type": "function",
+                    }
+                ]
+            },
+        ),
+        ToolMessage(content=str(context), tool_call_id=TOOL_ID),
+    ]
+    return messages
+
+
 chain = (
     RunnablePassthrough.assign(query=cypher_response)
     | RunnablePassthrough.assign(
-        response=lambda x: graph.query(cypher_validation(x["query"])),
+        function_response=lambda x: get_function_response(x["query"], x["question"]),
     )
     | RunnablePassthrough.assign(
         output=response_prompt | qa_llm | StrOutputParser(),