templates: Add neo4j semantic layer with ollama template (#17192)

A template with JSON-based agent using Mixtral via Ollama.

---------

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

templates/neo4j-semantic-ollama/neo4j_semantic_ollama/information_tool.py

@@ -0,0 +1,74 @@
+from typing import Optional, Type
+
+from langchain.callbacks.manager import (
+    AsyncCallbackManagerForToolRun,
+    CallbackManagerForToolRun,
+)
+
+# Import things that are needed generically
+from langchain.pydantic_v1 import BaseModel, Field
+from langchain.tools import BaseTool
+
+from neo4j_semantic_ollama.utils import get_candidates, graph
+
+description_query = """
+MATCH (m:Movie|Person)
+WHERE m.title = $candidate OR m.name = $candidate
+MATCH (m)-[r:ACTED_IN|DIRECTED|HAS_GENRE]-(t)
+WITH m, type(r) as type, collect(coalesce(t.name, t.title)) as names
+WITH m, type+": "+reduce(s="", n IN names | s + n + ", ") as types
+WITH m, collect(types) as contexts
+WITH m, "type:" + labels(m)[0] + "\ntitle: "+ coalesce(m.title, m.name) 
+       + "\nyear: "+coalesce(m.released,"") +"\n" +
+       reduce(s="", c in contexts | s + substring(c, 0, size(c)-2) +"\n") as context
+RETURN context LIMIT 1
+"""
+
+
+def get_information(entity: str, type: str) -> str:
+    candidates = get_candidates(entity, type)
+    if not candidates:
+        return "No information was found about the movie or person in the database"
+    elif len(candidates) > 1:
+        newline = "\n"
+        return (
+            "Need additional information, which of these "
+            f"did you mean: {newline + newline.join(str(d) for d in candidates)}"
+        )
+    data = graph.query(
+        description_query, params={"candidate": candidates[0]["candidate"]}
+    )
+    return data[0]["context"]
+
+
+class InformationInput(BaseModel):
+    entity: str = Field(description="movie or a person mentioned in the question")
+    entity_type: str = Field(
+        description="type of the entity. Available options are 'movie' or 'person'"
+    )
+
+
+class InformationTool(BaseTool):
+    name = "Information"
+    description = (
+        "useful for when you need to answer questions about various actors or movies"
+    )
+    args_schema: Type[BaseModel] = InformationInput
+
+    def _run(
+        self,
+        entity: str,
+        entity_type: str,
+        run_manager: Optional[CallbackManagerForToolRun] = None,
+    ) -> str:
+        """Use the tool."""
+        return get_information(entity, entity_type)
+
+    async def _arun(
+        self,
+        entity: str,
+        entity_type: str,
+        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
+    ) -> str:
+        """Use the tool asynchronously."""
+        return get_information(entity, entity_type)