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A template with JSON-based agent using Mixtral via Ollama. --------- Co-authored-by: Erick Friis <erick@langchain.dev>
105 lines
4.0 KiB
Markdown
105 lines
4.0 KiB
Markdown
# neo4j-semantic-ollama
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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.
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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.
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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).
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## Tools
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The agent utilizes several tools to interact with the Neo4j graph database effectively:
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1. **Information tool**:
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- Retrieves data about movies or individuals, ensuring the agent has access to the latest and most relevant information.
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2. **Recommendation Tool**:
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- Provides movie recommendations based upon user preferences and input.
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3. **Memory Tool**:
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- Stores information about user preferences in the knowledge graph, allowing for a personalized experience over multiple interactions.
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4. **Smalltalk Tool**:
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- Allows an agent to deal with smalltalk.
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## Environment Setup
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Before using this template, you need to set up Ollama and Neo4j database.
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1. Follow instructions [here](https://python.langchain.com/docs/integrations/chat/ollama) to download Ollama.
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2. Download your LLM of interest:
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* This package uses `mixtral`: `ollama pull mixtral`
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* You can choose from many LLMs [here](https://ollama.ai/library)
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You need to define the following environment variables
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```
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OLLAMA_BASE_URL=<YOUR_OLLAMA_URL>
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NEO4J_URI=<YOUR_NEO4J_URI>
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NEO4J_USERNAME=<YOUR_NEO4J_USERNAME>
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NEO4J_PASSWORD=<YOUR_NEO4J_PASSWORD>
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```
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## Populating with data
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If you want to populate the DB with an example movie dataset, you can run `python ingest.py`.
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The script import information about movies and their rating by users.
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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.
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## Usage
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To use this package, you should first have the LangChain CLI installed:
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```shell
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pip install -U "langchain-cli[serve]"
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```
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To create a new LangChain project and install this as the only package, you can do:
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```shell
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langchain app new my-app --package neo4j-semantic-ollama
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```
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If you want to add this to an existing project, you can just run:
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```shell
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langchain app add neo4j-semantic-ollama
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```
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And add the following code to your `server.py` file:
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```python
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from neo4j_semantic_layer import agent_executor as neo4j_semantic_agent
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add_routes(app, neo4j_semantic_agent, path="/neo4j-semantic-ollama")
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```
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(Optional) Let's now configure LangSmith.
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LangSmith will help us trace, monitor and debug LangChain applications.
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LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/).
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If you don't have access, you can skip this section
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```shell
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export LANGCHAIN_TRACING_V2=true
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export LANGCHAIN_API_KEY=<your-api-key>
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export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
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```
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If you are inside this directory, then you can spin up a LangServe instance directly by:
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```shell
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langchain serve
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```
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This will start the FastAPI app with a server is running locally at
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[http://localhost:8000](http://localhost:8000)
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We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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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)
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We can access the template from code with:
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```python
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from langserve.client import RemoteRunnable
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runnable = RemoteRunnable("http://localhost:8000/neo4j-semantic-ollama")
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```
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