Enable Boolean clauses in the Elm frontend for Elasticsearch

nixos-search-boolean.txt

### This is 100% Copilot responses to my queries - Expect lot's of errors and random WTF moments ###
### Copilot was given my fork of nixos-search to evaluate for it's replies ###
### Enable Boolean clauses in the Elm frontend for Elasticsearch ###

    # Extend the Elm model to represent Boolean logic.
    # Update the query encoder to generate the appropriate JSON for Elasticsearch.
    # Adjust the update logic if needed to support new query types.

### Extend the Elm Model ###

# In the Search.elm, define a new type to represent Boolean clauses:

type BoolClause
    = Must MatchClause
    | Should MatchClause
    | MustNot MatchClause
    | Filter MatchClause

type alias MatchClause =
    { field : String
    , value : String
    }


# Update the Model to include a list of these clauses:

type alias Model =
    { ...
    , boolClauses : List BoolClause
    }


# Update the JSON Encoder


import Json.Encode exposing (..)

encodeBoolClause : BoolClause -> ( String, Value )
encodeBoolClause clause =
    case clause of
        Must match ->
            ( "must", list [ encodeMatch match ] )

        Should match ->
            ( "should", list [ encodeMatch match ] )

        MustNot match ->
            ( "must_not", list [ encodeMatch match ] )

        Filter match ->
            ( "filter", list [ encodeMatch match ] )

encodeMatch : MatchClause -> Value
encodeMatch match =
    object
        [ ( "match", object [ ( match.field, string match.value ) ] ) ]

### Add a function to encode the BoolClause list into a JSON object that Elasticsearch understands ###

# In the main query encoder:

encodeBoolQuery : List BoolClause -> Value
encodeBoolQuery clauses =
    let
        grouped =
            Dict.fromListWith (++) (List.map (\c -> let (k, v) = encodeBoolClause c in (k, [v])) clauses)
    in
    object
        [ ( "query"
          , object
                [ ( "bool"
                  , object (Dict.toList (Dict.map (\_ vs -> list vs) grouped))
                  )
                ]
          )
        ]

# Update the View and Update Logic

### Allow users to add clauses via the UI.###

# For example, buttons to add a Must, Should, etc., clause. 
# In the update function, handle those actions by appending to model.boolClauses.


### To build a UI in Elm that allows users to add Boolean clauses (must, should, must_not, filter) to an Elasticsearch query ###

    Define messages for user actions.
    Update the model to track input fields.
    Create view elements for input and buttons.
    Handle updates to modify the model and trigger query encoding

# Define Messages

# Add these to the Msg type:

type Msg
    = UpdateField String
    | UpdateValue String
    | AddClause String


# Extend the Model

# Add temporary fields for user input:

type alias Model =
    { ...
    , currentField : String
    , currentValue : String
    , boolClauses : List BoolClause
    }


# Initialize them in the init function:

init : Model
init =
    { ...
    , currentField = ""
    , currentValue = ""
    , boolClauses = []
    }

### Create the View ###

# Add a form to input field/value and buttons to add clauses:

view : Model -> Html Msg
view model =
    div []
        [ input [ placeholder "Field", onInput UpdateField ] []
        , input [ placeholder "Value", onInput UpdateValue ] []
        , button [ onClick (AddClause "must") ] [ text "Add MUST" ]
        , button [ onClick (AddClause "should") ] [ text "Add SHOULD" ]
        , button [ onClick (AddClause "must_not") ] [ text "Add MUST NOT" ]
        , button [ onClick (AddClause "filter") ] [ text "Add FILTER" ]
        , div [] (List.map viewClause model.boolClauses)
        ]


# Add a helper to display added clauses:

viewClause : BoolClause -> Html msg
viewClause clause =
    case clause of
        Must m -> text ("MUST: " ++ m.field ++ " = " ++ m.value)
        Should m -> text ("SHOULD: " ++ m.field ++ " = " ++ m.value)
        MustNot m -> text ("MUST NOT: " ++ m.field ++ " = " ++ m.value)
        Filter m -> text ("FILTER: " ++ m.field ++ " = " ++ m.value)

### Handle Updates ###

# Update the update function:

update : Msg -> Model -> ( Model, Cmd Msg )
update msg model =
    case msg of
        UpdateField field ->
            ( { model | currentField = field }, Cmd.none )

        UpdateValue value ->
            ( { model | currentValue = value }, Cmd.none )

        AddClause clauseType ->
            let
                match = { field = model.currentField, value = model.currentValue }
                newClause =
                    case clauseType of
                        "must" -> Must match
                        "should" -> Should match
                        "must_not" -> MustNot match
                        "filter" -> Filter match
                        _ -> Must match
            in
            ( { model
                | boolClauses = model.boolClauses ++ [ newClause ]
                , currentField = ""
                , currentValue = ""
              }
            , Cmd.none
            )


### To integrate the updated Boolean clause UI with the Elasticsearch backend ###

    Encode the boolClauses into a JSON query.
    Send the query to the backend using an HTTP request.
    Handle the response and update the model with results.

# Encode the Query

# Use the encodeBoolQuery function (from earlier). Use it to convert model.boolClauses into a JSON payload:

import Json.Encode exposing (encode)

sendQuery : Model -> Cmd Msg
sendQuery model =
    let
        body =
            encode 0 (encodeBoolQuery model.boolClauses)
    in
    Http.post
        { url = "/the-elasticsearch-endpoint"
        , body = Http.jsonBody (encodeBoolQuery model.boolClauses)
        , expect = Http.expectJson GotSearchResults searchResultsDecoder
        }


#Replace "/the-elasticsearch-endpoint" with the actual URL the backend exposes for search.

### Define the Response Decoder ###

# Assuming the Elasticsearch response includes a list of hits:

type alias SearchResult =
    { title : String
    , snippet : String
    }

searchResultsDecoder : Decoder (List SearchResult)
searchResultsDecoder =
    Decode.at [ "hits", "hits" ]
        (Decode.list
            (Decode.map2 SearchResult
                (Decode.at [ "_source", "title" ] Decode.string)
                (Decode.at [ "_source", "snippet" ] Decode.string)
            )
        )

### Add a Message and Update Logic ###

# Extend the Msg type:

type Msg
    = ...
    | GotSearchResults (Result Http.Error (List SearchResult))

# Update the update function:

update msg model =
    case msg of
        ...
        AddClause clauseType ->
            let
                ...
            in
            ( updatedModel, sendQuery updatedModel )

        GotSearchResults result ->
            case result of
                Ok results ->
                    ( { model | searchResults = results }, Cmd.none )

                Err _ ->
                    ( model, Cmd.none ) -- You can add error handling here


### Display the Results ###

# Add a searchResults field to the Model:

type alias Model =
    { ...
    , searchResults : List SearchResult
    }


# Initialize it as an empty list, and render it in the view:

view model =
    div []
        [ ...
        , div [] (List.map viewResult model.searchResults)
        ]

viewResult : SearchResult -> Html msg
viewResult result =
    div []
        [ h3 [] [ text result.title ]
        , p [] [ text result.snippet ]
        ]
        
### Define the Elasticsearch schema and integrate it with the Elm frontend ###

# Elasticsearch Document Schema (for Nix Packages)

# Example of each document in the Elasticsearch index might look like:

{
  "name": "firefox",
  "version": "123.0",
  "description": "A fast, privacy-focused web browser",
  "homepage": "https://www.mozilla.org/firefox",
  "source": "https://github.com/mozilla/firefox",
  "programs": ["firefox"],
  "install": {
    "nix-shell": "nix-shell -p firefox",
    "nixos": "services.firefox.enable = true;",
    "nix-env": "nix-env -iA nixos.firefox"
  },
  "license": "MPL-2.0",
  "maintainers": ["alice", "bob"],
  "platforms": ["x86_64-linux", "aarch64-linux"]
}

### Search Fields ###

# To search by:

#    Name (e.g., "firefox")
#    Version (e.g., "123.0")

# The Elasticsearch query should look like this:

{
  "query": {
    "bool": {
      "must": [
        { "match": { "name": "firefox" } },
        { "match": { "version": "123.0" } }
      ]
    }
  }
}


### Elm Decoder for Search Results ###

# Here’s how to decode the response in Elm:

type alias SearchResult =
    { name : String
    , version : String
    , description : String
    , homepage : String
    , license : String
    }

searchResultsDecoder : Decoder (List SearchResult)
searchResultsDecoder =
    Decode.at [ "hits", "hits" ]
        (Decode.list
            (Decode.map5 SearchResult
                (Decode.at [ "_source", "name" ] Decode.string)
                (Decode.at [ "_source", "version" ] Decode.string)
                (Decode.at [ "_source", "description" ] Decode.string)
                (Decode.at [ "_source", "homepage" ] Decode.string)
                (Decode.at [ "_source", "license" ] Decode.string)
            )
        )


# It's posible to expand this decoder to include maintainers, platforms, and install instructions if needed.

# Here’s a sample Elasticsearch index mapping tailored to the Nix packages schema. 
# This mapping ensures that fields like name and version are searchable, while others like platforms and maintainers are stored appropriately for filtering or display.

### Elasticsearch Index Mapping for Nix Packages ###

PUT /nixpkgs-packages
{
  "mappings": {
    "properties": {
      "name": {
        "type": "text",
        "fields": {
          "keyword": { "type": "keyword" }
        }
      },
      "version": {
        "type": "text",
        "fields": {
          "keyword": { "type": "keyword" }
        }
      },
      "description": {
        "type": "text"
      },
      "homepage": {
        "type": "keyword"
      },
      "source": {
        "type": "keyword"
      },
      "programs": {
        "type": "keyword"
      },
      "install": {
        "properties": {
          "nix-shell": { "type": "text" },
         " }
        }
      },
      "license": {
        "type": "keyword"
      },
      "maintainers": {
        "type": "keyword"
      },
      "platforms": {
        "type": "keyword"
      }
    }
  }
}

### Notes ###

    # text is used for full-text search (e.g., name, description).
    # keyword is used for exact matches and aggregations (e.g., license, platforms).
    # The fields.keyword subfield allows you to do both full-text and exact match queries on name and version.

### A script to bulk index the Nix package data into this schema ###

# Bulk index the Nix package data into Elasticsearch:

    # Prepare the data in the Elasticsearch Bulk API format.
    # Use a script to send the data to the Elasticsearch instance.

# Format The Data

# Each document should be preceded by a metadata line like this:

{ "index": { "_index": "nixpkgs-packages" } }
{ "name": "firefox", "version": "123.0", ... }
{ "index": { "_index": "nixpkgs-packages" } }
{ "name": "vim", "version": "9.0", ... }

# Python Script to Bulk Index

# Here's a Python script that:
# Reads a JSON file with the package data. 
# Converts it to the correct bulk format. 
# Sends it to the Elasticsearch instance.

import json
import requests

# Load the JSON file containing Nix package data
with open('nix_packages.json', 'r') as file:
    packages = json.load(file)

# Prepare the bulk indexing payload
bulk_payload = ""
for package in packages:
    action = json.dumps({ "index": { "_index": "nixpkgs-packages" } })
    data = json.dumps(package)
    bulk_payload += f"{action}\n{data}\n"

# Send the bulk request to Elasticsearch
response = requests.post(
    'http://localhost:9200/_bulk',
    headers={ 'Content-Type': 'application/x-ndjson' },
    data=bulk_payload
)


### To test the full query flow from Elm to Elasticsearch ###

    # The Elm frontend correctly builds and sends the query.
    # The Elasticsearch backend receives and processes the query.
    # The response is correctly decoded and displayed in the UI.

# Test the Elm Query Encoder
# In Elm, log the encoded query before sending it:
# This helps to verify that the JSON structure matches what Elasticsearch expects.

Debug.log "Encoded Query" (encode 2 (encodeBoolQuery model.boolClauses))


# Use browser dev tools or a proxy like mitmproxy or Charles Proxy to inspect the actual HTTP request sent from Elm. 
#Check:

    # URL is correct (e.g., http://localhost:9200/nixpkgs-packages/_search)
    # Method is POST
    # Headers include Content-Type: application/json
    # Body matches the expected Elasticsearch query

# To test Elasticsearch directly, use curl or Postman to test the query manually:

curl -X POST "http://localhost:9200/nixpkgs-packages/_search" \
  -H "Content-Type: application/json" \
  -d '{
    "query": {
      "bool": {
        "must": [
          { "match": { "name": "firefox" } },
          { "match": { "version": "123.0" } }
        ]
      }
    }
  }'


# If this returns results, the index and mapping are working.

# Validate Elm Decoder

# In Elm, log the decoded results:

Debug.log "Search Results" model.searchResults

# If decoding fails, check the structure of the Elasticsearch response and adjust the decoder accordingly.

### Troubleshooting Tips ###

    # If there are no results, try simplifying the query (e.g., just match on name).
    # If decoding fails, use Http.expectString temporarily to inspect the raw response.
    # Make sure CORS is enabled on the Elasticsearch server if the frontend is served from a different origin.


### Mock Elasticsearch response to use to test the Elm decoder ###

#This simulates what Elasticsearch would return for a query on the nixpkgs-packages index:

{
  "took": 5,
  "timed_out": false,
  "_shards": {
    "total": 1,
    "successful": 1,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": {
      "value": 1,
      "relation": "eq"
    },
    "max_score": 1.0,
    "hits": [
      {
        "_index": "nixpkgs-packages",
        "_id": "firefox-123.0",
        "_score": 1.0,
        "_source": {
          "name": "firefox",
          "version": "123.0",
          "description": "A fast, privacy-focused web browser",
          "homepage": "https://www.mozilla.org/firefox",
          "license": "MPL-2.0"
        }
      }
    ]
  }
}

# How to Use This

    # Save it as a file (e.g., mock-response.json).
    # In Elm, temporarily replace the Http.expectJson with Http.expectString to log the raw response.
    # Use a local server or mock HTTP library to serve this file when testing.

### Mock HTTP server script (e.g., in Python or Node.js) to serve this JSON locally for testing ###

import http.server
import socketserver
import json

PORT = 8000

class MockElasticsearchHandler(http.server.SimpleHTTPRequestHandler):
    def do_GET(self):
        if self.path == '/mock-response.json':
            self.send_response(200)
            self.send_header('Content-type', 'application/json')
            self.end_headers()
            with open('mock-response.json', 'r') as file:
                response_content = file.read()
                self.wfile.write(response_content.encode())
        else:
            self.send_response(404)
            self.end_headers()

Handler = MockElasticsearchHandler

with socketserver.TCPServer(("", PORT), Handler) as httpd:
    print(f"Serving at port {PORT}")
    httpd.serve_forever()

### Inspection of the current repository shows a few areas to double-check ###

# 1. Frontend Query Construction (Elm)

    # The file frontend/src/Search.elm appears to build queries manually.
    # If adding Boolean logic, ensure the encodeBoolQuery function is correctly integrated and replaces any older query logic.

# 2. Backend Proxy or API Layer

    # If the frontend is not querying Elasticsearch directly, there may be a backend proxy that transforms or filters requests.
    # Check for any middleware that might strip or alter the query payload.

# 3. CORS Configuration

    # If running the frontend separately from Elasticsearch, ensure CORS is enabled on the Elasticsearch server: 
    
# 4. Index Name and Mapping

    # Confirm that the index name used in the frontend matches the one in Elasticsearch (nixpkgs-packages or similar).
    # Ensure the mapping supports the fields you're querying (name, version, etc.).

# 5. Data Availability

    # If the queries return no results, verify that the data is actually indexed and matches the query terms.
    # Try a simple match_all query to confirm data presence.