nlp_server

NLP server housing intent and NER models as well as Langchain memory agent for Ditto assistant clients.

Running Locally with Docker

1. Rename .env.example to .env and set OPENAI_API_KEY to your OpenAI API key.
2. Rename example_users.json to users.json and add user's information.
3. docker build -t nlp_server .
4. docker run --env-file .env --rm -p 32032:32032 nlp_server

Set up Google Search API for LLM (optional)

1. Main Google Search Agent:
   1. Create an account on serpapi.com and set SERPAPI_API_KEY to your API key in .env.
2. Fallback Agent:
   1. Create an account on serper.dev and set SERPER_API_KEY to your API key in .env.

Changing LLM Provider

1. If you prefer using HuggingFace's API, set HUGGINGFACEHUB_API_TOKEN to your HuggingFace API key and set LLM=huggingface in .env.

Visualizing Memory

1. Install Neo4j Desktop and create a new project.
2. Add a new database named ditto-memory and set the password to password.
3. That's it! You can now visualize the memory by opening the Neo4j browser and running the following query:

MATCH (n) RETURN n LIMIT 100

Memory Visualization in Neo4j Example

All nodes can be expanded to view their properties. Each node holds textual information in the description property. The following are the types of nodes in the graph and what their description properties hold:

1. Prompt Nodes: holds a summary of the user's prompt in description.
2. Response Nodes: holds a summary of Ditto's response in description.
3. Subject Nodes: holds the subject name of the conversation pair in title and description (same as title for Subject Nodes).
4. Sub-category Nodes: holds a summary of each sub-category in Ditto's response, where each sub-category node holds a summary of the sub-category in description.