couchbase-examples / qa-bot-demo

Q&A Chatbot Demo using Couchbase, LangChain, OpenAI and Streamlit

Q&A ChatBot Demo using Couchbase, Streamlit, LangChain, LangSmith and OpenAI

This is a demo Q&A ChatBot built to chat with documentation websites using the Vector Search capabilities of Couchbase to augment the OpenAI results in a Retrieval-Augmented-Generation (RAG) model. It uses LangChain to orchestrate the entire workflow.

Note that you need Couchbase Server 7.6 or higher for Vector Search.

How does it work?

You can ask questions about the documentation that has been indexed into the Vector Store. You will get answers from the documentation along with some additional sources to get more information from the documentation website.

You can also ask follow up questions. The chat bot will summarize the previous chat history to find relevant answers.

We are using Streamlit, LangChain, Couchbase Vector Search & OpenAI along with LangSmith for observability into the bot.

Note that currently the chat history is not linked to any user. Hence, when multiple users are using the application, you might need to get rid of the chat history by clicking on the "Clear Chat Context" in the UI.

How to Run

• Install dependencies

  pip install -r requirements.txt

• Set the environment secrets

  Copy the secrets.example.toml file and rename it to secrets.toml and replace the placeholders with the actual values for your environment.

  For the ingestion of documentation, the same environment variables need to be set in the environment (using .env file from .env.example) as it runs outside the Streamlit environment.

  OPENAI_API_KEY = "<open_ai_api_key>"
  DB_CONN_STR = "<connection_string_for_couchbase_cluster>"
  DB_USERNAME = "<username_for_couchbase_cluster>"
  DB_PASSWORD = "<password_for_couchbase_cluster>"
  DB_BUCKET = "<name_of_bucket_to_store_documents>"
  DB_SCOPE = "<name_of_scope_to_store_documents>"
  DB_COLLECTION = "<name_of_collection_to_store_documents>"
  INDEX_NAME = "<name_of_fts_index_with_vector_support>"
  EMBEDDING_MODEL = "text-embedding-3-small" # OpenAI embedding model to use to encode the documents
  LANGCHAIN_ENDPOINT = "https://api.smith.langchain.com"
  LANGCHAIN_API_KEY = "<api_key_for_langsmith>"
  AUTH_ENABLED = "True/False" # enables authentication for the streamlit app using LOGIN_PASSWORD
  LOGIN_PASSWORD = "<password to access the streamlit app>"
  

• Create a List of URLs to Index into Vector Store

  We need to index the documents that the Q&A ChatBot can answer. For this, there in an ingestion script, ingest_docs.py that can be used to ingest the documents into Couchbase Vector Store.

  For the list of documents, you can use the template file_with_urls.json.example. You can copy the file into file_with_urls.json and add your list of URLs to support.

  The file has the format of one URL per line of the document like the following:

  [
    {
      "URL": "https://docs.couchbase.com/cloud/index.html"
    },
    {
      "URL": "https://docs.couchbase.com/cloud/index.html"
    }
  ]

  The list of URLs supported by the ChatBot can be determined possibly using the website's sitemap.

• Create the Search Index on Full Text Service

  We need to create the Search Index on the Full Text Service in Couchbase. For this demo, you can import the following index using the instructions.

  • Couchbase Capella

    • Copy the index definition to a new file index.json
    • Import the file in Capella using the instructions in the documentation.
    • Click on Create Index to create the index.

  • Couchbase Server

    • Click on Search -> Add Index -> Import
    • Copy the following Index definition in the Import screen
    • Click on Create Index to create the index.

  Index Definition

  Here, we are creating the index docs-search on the documents in the _default collection within the _default scope in the bucket docs. The Vector field is set to embeddings with 1536 dimensions and the text field set to text. We are also indexing and storing all the fields under metadata in the document as a dynamic mapping to account for varying document structures. The similarity metric is set to dot_product. If there is a change in these parameters, please adapt the index accordingly.

  {
    "type": "fulltext-index",
    "name": "docs._default.docs-search",
    "uuid": "2aefca04031a299d",
    "sourceType": "gocbcore",
    "sourceName": "docs",
    "planParams": {
      "maxPartitionsPerPIndex": 1024,
      "indexPartitions": 1
    },
    "params": {
      "doc_config": {
        "docid_prefix_delim": "",
        "docid_regexp": "",
        "mode": "scope.collection.type_field",
        "type_field": "type"
      },
      "mapping": {
        "analysis": {},
        "default_analyzer": "standard",
        "default_datetime_parser": "dateTimeOptional",
        "default_field": "_all",
        "default_mapping": {
          "dynamic": false,
          "enabled": false
        },
        "default_type": "_default",
        "docvalues_dynamic": false,
        "index_dynamic": true,
        "store_dynamic": true,
        "type_field": "_type",
        "types": {
          "_default._default": {
            "dynamic": false,
            "enabled": true,
            "properties": {
              "embedding": {
                "dynamic": false,
                "enabled": true,
                "fields": [
                  {
                    "dims": 1536,
                    "index": true,
                    "name": "embedding",
                    "similarity": "dot_product",
                    "type": "vector",
                    "vector_index_optimized_for": "recall"
                  }
                ]
              },
              "metadata": {
                "dynamic": true,
                "enabled": true
              },
              "text": {
                "dynamic": false,
                "enabled": true,
                "fields": [
                  {
                    "analyzer": "en",
                    "index": true,
                    "name": "text",
                    "store": true,
                    "type": "text"
                  }
                ]
              }
            }
          }
        }
      },
      "store": {
        "indexType": "scorch",
        "segmentVersion": 16
      }
    },
    "sourceParams": {}
  }

• Ingest the Documents

  python ingest_docs.py

• Run the Application

  streamlit run bot_ui.py