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/README.md

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+# neo4j-semantic-ollama
+
+This template is designed to implement an agent capable of interacting with a graph database like Neo4j through a semantic layer using Mixtral as a JSON-based agent.
+The semantic layer equips the agent with a suite of robust tools, allowing it to interact with the graph database based on the user's intent.
+Learn more about the semantic layer template in the [corresponding blog post](https://medium.com/towards-data-science/enhancing-interaction-between-language-models-and-graph-databases-via-a-semantic-layer-0a78ad3eba49).
+
+![Diagram illustrating the workflow of the Neo4j semantic layer with an agent interacting with tools like Information, Recommendation, and Memory, connected to a knowledge graph.](https://raw.githubusercontent.com/langchain-ai/langchain/master/templates/neo4j-semantic-ollama/static/workflow.png "Neo4j Semantic Layer Workflow Diagram")
+
+## Tools
+
+The agent utilizes several tools to interact with the Neo4j graph database effectively:
+
+1. **Information tool**:
+   - Retrieves data about movies or individuals, ensuring the agent has access to the latest and most relevant information.
+2. **Recommendation Tool**:
+   - Provides movie recommendations based upon user preferences and input.
+3. **Memory Tool**:
+   - Stores information about user preferences in the knowledge graph, allowing for a personalized experience over multiple interactions.
+4. **Smalltalk Tool**:
+   - Allows an agent to deal with smalltalk.
+
+## Environment Setup
+
+Before using this template, you need to set up Ollama and Neo4j database.
+
+1. Follow instructions [here](https://python.langchain.com/docs/integrations/chat/ollama) to download Ollama.
+
+2. Download your LLM of interest:
+
+    * This package uses `mixtral`: `ollama pull mixtral`
+    * You can choose from many LLMs [here](https://ollama.ai/library)
+
+You need to define the following environment variables
+
+```
+OLLAMA_BASE_URL=<YOUR_OLLAMA_URL>
+NEO4J_URI=<YOUR_NEO4J_URI>
+NEO4J_USERNAME=<YOUR_NEO4J_USERNAME>
+NEO4J_PASSWORD=<YOUR_NEO4J_PASSWORD>
+```
+
+## Populating with data
+
+If you want to populate the DB with an example movie dataset, you can run `python ingest.py`.
+The script import information about movies and their rating by users.
+Additionally, the script creates two [fulltext indices](https://neo4j.com/docs/cypher-manual/current/indexes-for-full-text-search/), which are used to map information from user input to the database.
+
+## Usage
+
+To use this package, you should first have the LangChain CLI installed:
+
+```shell
+pip install -U "langchain-cli[serve]"
+```
+
+To create a new LangChain project and install this as the only package, you can do:
+
+```shell
+langchain app new my-app --package neo4j-semantic-ollama
+```
+
+If you want to add this to an existing project, you can just run:
+
+```shell
+langchain app add neo4j-semantic-ollama
+```
+
+And add the following code to your `server.py` file:
+```python
+from neo4j_semantic_layer import agent_executor as neo4j_semantic_agent
+
+add_routes(app, neo4j_semantic_agent, path="/neo4j-semantic-ollama")
+```
+
+(Optional) Let's now configure LangSmith. 
+LangSmith will help us trace, monitor and debug LangChain applications. 
+LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/). 
+If you don't have access, you can skip this section
+
+```shell
+export LANGCHAIN_TRACING_V2=true
+export LANGCHAIN_API_KEY=<your-api-key>
+export LANGCHAIN_PROJECT=<your-project>  # if not specified, defaults to "default"
+```
+
+If you are inside this directory, then you can spin up a LangServe instance directly by:
+
+```shell
+langchain serve
+```
+
+This will start the FastAPI app with a server is running locally at 
+[http://localhost:8000](http://localhost:8000)
+
+We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
+We can access the playground at [http://127.0.0.1:8000/neo4j-semantic-ollama/playground](http://127.0.0.1:8000/neo4j-semantic-ollama/playground)  
+
+We can access the template from code with:
+
+```python
+from langserve.client import RemoteRunnable
+
+runnable = RemoteRunnable("http://localhost:8000/neo4j-semantic-ollama")
+```

templates/neo4j-semantic-ollama/ingest.py

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+from langchain_community.graphs import Neo4jGraph
+
+# Instantiate connection to Neo4j
+graph = Neo4jGraph()
+
+# Define unique constraints
+graph.query("CREATE CONSTRAINT IF NOT EXISTS FOR (m:Movie) REQUIRE m.id IS UNIQUE;")
+graph.query("CREATE CONSTRAINT IF NOT EXISTS FOR (u:User) REQUIRE u.id IS UNIQUE;")
+graph.query("CREATE CONSTRAINT IF NOT EXISTS FOR (p:Person) REQUIRE p.name IS UNIQUE;")
+graph.query("CREATE CONSTRAINT IF NOT EXISTS FOR (g:Genre) REQUIRE g.name IS UNIQUE;")
+
+# Import movie information
+
+movies_query = """
+LOAD CSV WITH HEADERS FROM 
+'https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/movies/movies.csv'
+AS row
+CALL {
+    WITH row
+    MERGE (m:Movie {id:row.movieId})
+    SET m.released = date(row.released),
+        m.title = row.title,
+        m.imdbRating = toFloat(row.imdbRating)
+    FOREACH (director in split(row.director, '|') | 
+        MERGE (p:Person {name:trim(director)})
+        MERGE (p)-[:DIRECTED]->(m))
+    FOREACH (actor in split(row.actors, '|') | 
+        MERGE (p:Person {name:trim(actor)})
+        MERGE (p)-[:ACTED_IN]->(m))
+    FOREACH (genre in split(row.genres, '|') | 
+        MERGE (g:Genre {name:trim(genre)})
+        MERGE (m)-[:IN_GENRE]->(g))
+} IN TRANSACTIONS
+"""
+
+graph.query(movies_query)
+
+# Import rating information
+rating_query = """
+LOAD CSV WITH HEADERS FROM 
+'https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/movies/ratings.csv'
+AS row
+CALL {
+    WITH row
+    MATCH (m:Movie {id:row.movieId})
+    MERGE (u:User {id:row.userId})
+    MERGE (u)-[r:RATED]->(m)
+    SET r.rating = toFloat(row.rating),
+        r.timestamp = row.timestamp
+} IN TRANSACTIONS OF 10000 ROWS
+"""
+
+graph.query(rating_query)
+
+# Define fulltext indices
+graph.query("CREATE FULLTEXT INDEX movie IF NOT EXISTS FOR (m:Movie) ON EACH [m.title]")
+graph.query(
+    "CREATE FULLTEXT INDEX person IF NOT EXISTS FOR (p:Person) ON EACH [p.name]"
+)

templates/neo4j-semantic-ollama/main.py

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+from neo4j_semantic_ollama import agent_executor
+
+if __name__ == "__main__":
+    original_query = "What do you know about person John?"
+    followup_query = "John Travolta"
+    chat_history = [
+        (
+            "What do you know about person John?",
+            "I found multiple people named John. Could you please specify "
+            "which one you are interested in? Here are some options:"
+            "\n\n1. John Travolta\n2. John McDonough",
+        )
+    ]
+    print(agent_executor.invoke({"input": original_query}))
+    print(
+        agent_executor.invoke({"input": followup_query, "chat_history": chat_history})
+    )

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

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+from neo4j_semantic_ollama.agent import agent_executor
+
+__all__ = ["agent_executor"]

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

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+import os
+from typing import List, Tuple
+
+from langchain.agents import AgentExecutor
+from langchain.agents.format_scratchpad import format_log_to_messages
+from langchain.agents.output_parsers import (
+    ReActJsonSingleInputOutputParser,
+)
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.pydantic_v1 import BaseModel, Field
+from langchain.schema import AIMessage, HumanMessage
+from langchain.tools.render import render_text_description_and_args
+from langchain_community.chat_models import ChatOllama
+
+from neo4j_semantic_ollama.information_tool import InformationTool
+from neo4j_semantic_ollama.memory_tool import MemoryTool
+from neo4j_semantic_ollama.recommendation_tool import RecommenderTool
+from neo4j_semantic_ollama.smalltalk_tool import SmalltalkTool
+
+llm = ChatOllama(
+    model="mixtral",
+    temperature=0,
+    base_url=os.environ["OLLAMA_BASE_URL"],
+    streaming=True,
+)
+chat_model_with_stop = llm.bind(stop=["\nObservation"])
+tools = [InformationTool(), RecommenderTool(), MemoryTool(), SmalltalkTool()]
+
+# Inspiration taken from hub.pull("hwchase17/react-json")
+system_message = f"""Answer the following questions as best you can.
+You can answer directly if the user is greeting you or similar.
+Otherise, you have access to the following tools:
+
+{render_text_description_and_args(tools).replace('{', '{{').replace('}', '}}')}
+
+The way you use the tools is by specifying a json blob.
+Specifically, this json should have a `action` key (with the name of the tool to use)
+and a `action_input` key (with the input to the tool going here).
+The only values that should be in the "action" field are: {[t.name for t in tools]}
+The $JSON_BLOB should only contain a SINGLE action, 
+do NOT return a list of multiple actions.
+Here is an example of a valid $JSON_BLOB:
+```
+{{{{
+    "action": $TOOL_NAME,
+    "action_input": $INPUT
+}}}}
+```
+The $JSON_BLOB must always be enclosed with triple backticks!
+
+ALWAYS use the following format:
+Question: the input question you must answer
+Thought: you should always think about what to do
+Action:```
+$JSON_BLOB
+```
+Observation: the result of the action... 
+(this Thought/Action/Observation can repeat N times)
+Thought: I now know the final answer
+Final Answer: the final answer to the original input question
+
+Begin! Reminder to always use the exact characters `Final Answer` when responding.'
+"""
+
+prompt = ChatPromptTemplate.from_messages(
+    [
+        (
+            "user",
+            system_message,
+        ),
+        MessagesPlaceholder(variable_name="chat_history"),
+        ("user", "{input}"),
+        MessagesPlaceholder(variable_name="agent_scratchpad"),
+    ]
+)
+
+
+def _format_chat_history(chat_history: List[Tuple[str, str]]):
+    buffer = []
+    for human, ai in chat_history:
+        buffer.append(HumanMessage(content=human))
+        buffer.append(AIMessage(content=ai))
+    return buffer
+
+
+agent = (
+    {
+        "input": lambda x: x["input"],
+        "agent_scratchpad": lambda x: format_log_to_messages(x["intermediate_steps"]),
+        "chat_history": lambda x: _format_chat_history(x["chat_history"])
+        if x.get("chat_history")
+        else [],
+    }
+    | prompt
+    | chat_model_with_stop
+    | ReActJsonSingleInputOutputParser()
+)
+
+
+# Add typing for input
+class AgentInput(BaseModel):
+    input: str
+    chat_history: List[Tuple[str, str]] = Field(
+        ..., extra={"widget": {"type": "chat", "input": "input", "output": "output"}}
+    )
+
+
+agent_executor = AgentExecutor(agent=agent, tools=tools).with_types(
+    input_type=AgentInput
+)

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

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+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)

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

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+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, get_user_id, graph
+
+store_rating_query = """
+MERGE (u:User {userId:$user_id}) 
+WITH u 
+UNWIND $candidates as row
+MATCH (m:Movie {title: row.candidate})
+MERGE (u)-[r:RATED]->(m)
+SET r.rating = toFloat($rating)
+RETURN distinct 
+   'Create a final answer saying that preference has been stored' AS response
+"""
+
+
+def store_movie_rating(movie: str, rating: int):
+    user_id = get_user_id()
+    candidates = get_candidates(movie, "movie")
+    if not candidates:
+        return "This movie is not in our database"
+    response = graph.query(
+        store_rating_query,
+        params={"user_id": user_id, "candidates": candidates, "rating": rating},
+    )
+    try:
+        return response[0]["response"]
+    except Exception as e:
+        print(e)
+        return "Something went wrong"
+
+
+class MemoryInput(BaseModel):
+    movie: str = Field(description="movie the user liked")
+    rating: int = Field(
+        description=(
+            "Rating from 1 to 5, where one represents heavy dislike "
+            "and 5 represent the user loved the movie"
+        )
+    )
+
+
+class MemoryTool(BaseTool):
+    name = "Memory"
+    description = "useful for memorizing which movies the user liked"
+    args_schema: Type[BaseModel] = MemoryInput
+
+    def _run(
+        self,
+        movie: str,
+        rating: int,
+        run_manager: Optional[CallbackManagerForToolRun] = None,
+    ) -> str:
+        """Use the tool."""
+        return store_movie_rating(movie, rating)
+
+    async def _arun(
+        self,
+        movie: str,
+        rating: int,
+        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
+    ) -> str:
+        """Use the tool asynchronously."""
+        return store_movie_rating(movie, rating)

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

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+from typing import Optional, Type
+
+from langchain.callbacks.manager import (
+    AsyncCallbackManagerForToolRun,
+    CallbackManagerForToolRun,
+)
+from langchain.pydantic_v1 import BaseModel, Field
+from langchain.tools import BaseTool
+
+from neo4j_semantic_ollama.utils import get_candidates, get_user_id, graph
+
+recommendation_query_db_history = """
+  MERGE (u:User {userId:$user_id})
+  WITH u
+  // get recommendation candidates
+  OPTIONAL MATCH (u)-[r1:RATED]->()<-[r2:RATED]-()-[r3:RATED]->(recommendation)
+  WHERE r1.rating > 3.5 AND r2.rating > 3.5 AND r3.rating > 3.5
+        AND NOT EXISTS {(u)-[:RATED]->(recommendation)}
+  // rank and limit recommendations
+  WITH u, recommendation, count(*) AS count
+  ORDER BY count DESC LIMIT 3
+RETURN 'title:' + recommendation.title + '\nactors:' +
+apoc.text.join([(recommendation)<-[:ACTED_IN]-(a) | a.name], ',') +
+'\ngenre:' + apoc.text.join([(recommendation)-[:IN_GENRE]->(a) | a.name], ',')
+AS movie
+"""
+
+recommendation_query_genre = """
+MATCH (m:Movie)-[:IN_GENRE]->(g:Genre {name:$genre})
+// filter out already seen movies by the user
+WHERE NOT EXISTS {
+  (m)<-[:RATED]-(:User {userId:$user_id})
+}
+// rank and limit recommendations
+WITH m AS recommendation
+ORDER BY recommendation.imdbRating DESC LIMIT 3
+RETURN 'title:' + recommendation.title + '\nactors:' +
+apoc.text.join([(recommendation)<-[:ACTED_IN]-(a) | a.name], ',') +
+'\ngenre:' + apoc.text.join([(recommendation)-[:IN_GENRE]->(a) | a.name], ',')
+AS movie
+"""
+
+
+def recommendation_query_movie(genre: bool) -> str:
+    return f"""
+MATCH (m1:Movie)<-[r1:RATED]-()-[r2:RATED]->(m2:Movie)
+WHERE r1.rating > 3.5 AND r2.rating > 3.5 and m1.title IN $movieTitles
+// filter out already seen movies by the user
+AND NOT EXISTS {{
+  (m2)<-[:RATED]-(:User {{userId:$user_id}})
+}}
+{'AND EXISTS {(m2)-[:IN_GENRE]->(:Genre {name:$genre})}' if genre else ''}
+// rank and limit recommendations
+WITH m2 AS recommendation, count(*) AS count
+ORDER BY count DESC LIMIT 3
+RETURN 'title:' + recommendation.title + '\nactors:' +
+apoc.text.join([(recommendation)<-[:ACTED_IN]-(a) | a.name], ',') +
+'\ngenre:' + apoc.text.join([(recommendation)-[:IN_GENRE]->(a) | a.name], ',')
+AS movie
+"""
+
+
+nl = "\n"
+
+
+def recommend_movie(movie: Optional[str] = None, genre: Optional[str] = None) -> str:
+    """
+    Recommends movies based on user's history and preference
+    for a specific movie and/or genre.
+    Returns:
+        str: A string containing a list of recommended movies, or an error message.
+    """
+    user_id = get_user_id()
+    params = {"user_id": user_id, "genre": genre}
+    if not movie and not genre:
+        # Try to recommend a movie based on the information in the db
+        response = graph.query(recommendation_query_db_history, params)
+        try:
+            return (
+                'Recommended movies are: '
+                f'{f"###Movie {nl}".join([el["movie"] for el in response])}'
+            )
+        except Exception:
+            return "Can you tell us about some of the movies you liked?"
+    if not movie and genre:
+        # Recommend top voted movies in the genre the user haven't seen before
+        response = graph.query(recommendation_query_genre, params)
+        try:
+            return (
+                'Recommended movies are: '
+                f'{f"###Movie {nl}".join([el["movie"] for el in response])}'
+            )
+        except Exception:
+            return "Something went wrong"
+
+    candidates = get_candidates(movie, "movie")
+    if not candidates:
+        return "The movie you mentioned wasn't found in the database"
+    params["movieTitles"] = [el["candidate"] for el in candidates]
+    query = recommendation_query_movie(bool(genre))
+    response = graph.query(query, params)
+    try:
+        return (
+            'Recommended movies are: '
+            f'{f"###Movie {nl}".join([el["movie"] for el in response])}'
+        )
+    except Exception:
+        return "Something went wrong"
+
+
+all_genres = [
+    "Action",
+    "Adventure",
+    "Animation",
+    "Children",
+    "Comedy",
+    "Crime",
+    "Documentary",
+    "Drama",
+    "Fantasy",
+    "Film-Noir",
+    "Horror",
+    "IMAX",
+    "Musical",
+    "Mystery",
+    "Romance",
+    "Sci-Fi",
+    "Thriller",
+    "War",
+    "Western",
+]
+
+
+class RecommenderInput(BaseModel):
+    movie: Optional[str] = Field(description="movie used for recommendation")
+    genre: Optional[str] = Field(
+        description=(
+            "genre used for recommendation. Available options are:" f"{all_genres}"
+        )
+    )
+
+
+class RecommenderTool(BaseTool):
+    name = "Recommender"
+    description = "useful for when you need to recommend a movie"
+    args_schema: Type[BaseModel] = RecommenderInput
+
+    def _run(
+        self,
+        movie: Optional[str] = None,
+        genre: Optional[str] = None,
+        run_manager: Optional[CallbackManagerForToolRun] = None,
+    ) -> str:
+        """Use the tool."""
+        return recommend_movie(movie, genre)
+
+    async def _arun(
+        self,
+        movie: Optional[str] = None,
+        genre: Optional[str] = None,
+        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
+    ) -> str:
+        """Use the tool asynchronously."""
+        return recommend_movie(movie, genre)

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

@@ -0,0 +1,39 @@
+from typing import Optional, Type
+
+from langchain.callbacks.manager import (
+    AsyncCallbackManagerForToolRun,
+    CallbackManagerForToolRun,
+)
+from langchain.pydantic_v1 import BaseModel, Field
+from langchain.tools import BaseTool
+
+response = (
+    "Create a final answer that says if they "
+    "have any questions about movies or actors"
+)
+
+
+class SmalltalkInput(BaseModel):
+    query: Optional[str] = Field(description="user query")
+
+
+class SmalltalkTool(BaseTool):
+    name = "Smalltalk"
+    description = "useful for when user greets you or wants to smalltalk"
+    args_schema: Type[BaseModel] = SmalltalkInput
+
+    def _run(
+        self,
+        query: Optional[str] = None,
+        run_manager: Optional[CallbackManagerForToolRun] = None,
+    ) -> str:
+        """Use the tool."""
+        return response
+
+    async def _arun(
+        self,
+        query: Optional[str] = None,
+        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
+    ) -> str:
+        """Use the tool asynchronously."""
+        return response

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

@@ -0,0 +1,84 @@
+from typing import Dict, List
+
+from langchain_community.graphs import Neo4jGraph
+
+graph = Neo4jGraph()
+
+
+def get_user_id() -> int:
+    """
+    Placeholder for a function that would normally retrieve
+    a user's ID
+    """
+    return 1
+
+
+def remove_lucene_chars(text: str) -> str:
+    """Remove Lucene special characters"""
+    special_chars = [
+        "+",
+        "-",
+        "&",
+        "|",
+        "!",
+        "(",
+        ")",
+        "{",
+        "}",
+        "[",
+        "]",
+        "^",
+        '"',
+        "~",
+        "*",
+        "?",
+        ":",
+        "\\",
+    ]
+    for char in special_chars:
+        if char in text:
+            text = text.replace(char, " ")
+    return text.strip()
+
+
+def generate_full_text_query(input: str) -> str:
+    """
+    Generate a full-text search query for a given input string.
+
+    This function constructs a query string suitable for a full-text search.
+    It processes the input string by splitting it into words and appending a
+    similarity threshold (~0.8) to each word, then combines them using the AND
+    operator. Useful for mapping movies and people from user questions
+    to database values, and allows for some misspelings.
+    """
+    full_text_query = ""
+    words = [el for el in remove_lucene_chars(input).split() if el]
+    for word in words[:-1]:
+        full_text_query += f" {word}~0.8 AND"
+    full_text_query += f" {words[-1]}~0.8"
+    return full_text_query.strip()
+
+
+candidate_query = """
+CALL db.index.fulltext.queryNodes($index, $fulltextQuery, {limit: $limit})
+YIELD node
+RETURN coalesce(node.name, node.title) AS candidate,
+       [el in labels(node) WHERE el IN ['Person', 'Movie'] | el][0] AS label
+"""
+
+
+def get_candidates(input: str, type: str, limit: int = 3) -> List[Dict[str, str]]:
+    """
+    Retrieve a list of candidate entities from database based on the input string.
+
+    This function queries the Neo4j database using a full-text search. It takes the
+    input string, generates a full-text query, and executes this query against the
+    specified index in the database. The function returns a list of candidates
+    matching the query, with each candidate being a dictionary containing their name
+    (or title) and label (either 'Person' or 'Movie').
+    """
+    ft_query = generate_full_text_query(input)
+    candidates = graph.query(
+        candidate_query, {"fulltextQuery": ft_query, "index": type, "limit": limit}
+    )
+    return candidates

templates/neo4j-semantic-ollama/pyproject.toml

@@ -0,0 +1,33 @@
+[tool.poetry]
+name = "neo4j-semantic-ollama"
+version = "0.1.0"
+description = "Build a semantic layer with Mixtral via Ollama to allow an agent to interact with a graph database in consistent and robust way."
+authors = [
+    "Tomaz Bratanic <tomaz.bratanic@neo4j.com>",
+]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.8.1,<4.0"
+langchain = "^0.1"
+openai = "<2"
+neo4j = "^5.14.0"
+
+[tool.poetry.group.dev.dependencies]
+langchain-cli = ">=0.0.21"
+
+[tool.langserve]
+export_module = "neo4j_semantic_ollama"
+export_attr = "agent_executor"
+
+[tool.templates-hub]
+use-case = "semantic_layer"
+author = "Neo4j"
+integrations = ["Neo4j", "Ollama"]
+tags = ["search", "graph-database", "function-calling"]
+
+[build-system]
+requires = [
+    "poetry-core",
+]
+build-backend = "poetry.core.masonry.api"