A chatbot powered by AI and RAG (retrieval augmented generation). The chatbot takes into account docs specified by the user in order to provide more accurate answers.

• Getting Started
  • Requirements
    • Ollama
    • Qdrant
  • Setting up and running the ChatBot App
    • Step 1 - Clone this repo
    • Step 2 - Change the config files according to the needs
    • Step 3 - Run the ChatBot App
• Using the Chatbot
  • Creating a collection
  • Adding documents to the vector database
  • Chating with the chat bot
• Built With
• Author

The following instructions will show how to prepare the environment and run the chatbot locally. Also, it will show how to use the chatbot.

• Golang
• Ollama
• Qdrant

Download Ollama and install it in your machine. Pull the desidered LLM (llama2 will be used as an example).

ollama pull llama2

Create a directory called qdrant_storage then pull/run the qdrant container image with the following command:

podman run -p 6333:6333 -p 6334:6334 -e QDRANT_SERVICE_GRPC_PORT="6334" -v $(pwd)/qdrant_storage:/qdrant/storage:z docker://qdrant/qdrant

This section will show how to prepare and run the chatbot on the local environment.

Clone this repo with the following command:

git clone https://github.com/aguidirh/go-rag-chatbot.git

There are two configuration files. Both are under the configs folder in the root of go-rag-chat go mod.

config.yaml

apiVersion: v1alpha1
kind: GoRagChatbotConfig
spec:
  qdrant:
    host: http://0.0.0.0
    port: 6333
    collection: oc-mirror
  llm:
    model: llama2
    scoreThreshold: 0.5
    temperature: 0.8
  server:
    port: 8080

kb-config.yaml - contains an array of resources to be consumed as additional knowledge base for the llm (large language model)

apiVersion: v1alpha1
kind: GoRagChatbotKnowledgeBaseConfig
spec:
  docs:
    - https://docs.openshift.com/container-platform/4.16/installing/disconnected_install/about-installing-oc-mirror-v2.html
    - https://github.com/openshift/oc-mirror/blob/main/v2/docs/enclave_support.md
    - /assets/kb-docs
    - https://github.com/openshift/oc-mirror/tree/main/v2/docs

The chatbot is exposed as an http server. Run the following command to bring it up:

go mod tidy
go run cmd/main.go

This section will show how to load the vector database with docs and how to interect with the chatbot

A collection is where the documents are going to be stored in the vector database. It is necessary to create a collection before adding documents to the database.

Since the chatbot is exposed as an http server, it is possible to create a new collection calling the following URL:

/create-collection?collection-name=test-collection

Here is an example of a call using cURL:

curl "http://localhost:8080/create-collection?collection-name=alex-test"

The chatbot does not know about specific things related to your business/domain. It is possible to add domain specific document calling the following URL:

/add-docs

Here is an example of a call using cURL:

curl "http://localhost:8080/add-docs"

After adding the domain documents to the vector database, it is possible to ask questions to the chatbot about specific domain subjects. With the help of the documents added in the previous step, it is more than likely that the chatbot will know how to answer the question.

The chatbot is exposed in the following URL:

/chat

Here is an example of a call using cURL:

curl "http://localhost:8080/chat"

• Golang
• LangChainGo
• Qdrant
• Ollama

• Alex Guidi - LinkedIn - GitHub