diff --git a/.github/labeler.yml b/.github/labeler.yml
index c19e1e9fb8..e9ecb0c45a 100644
--- a/.github/labeler.yml
+++ b/.github/labeler.yml
@@ -7,6 +7,8 @@ backend:
   - appserver/**/*
   - statistical-enrichment/*
   - statistical-enrichment/**/*
+  - llm/*
+  - llm/**/*
   - cache-invalidator/*
   - cache-invalidator/**/*
 
diff --git a/appserver/config.py b/appserver/config.py
index c094e85bb1..c007a196bc 100644
--- a/appserver/config.py
+++ b/appserver/config.py
@@ -72,6 +72,9 @@ class Base:
     SE_HOST = os.environ.get('SE_HOST', 'statistical-enrichment')
     SE_PORT = os.environ.get('SE_PORT', '5010')
 
+    LLM_HOST = os.environ.get('LLM_HOST', 'llm')
+    LLM_PORT = os.environ.get('LLM_PORT', '5000')
+
     NLP_SERVICE_ENDPOINT = os.environ.get(
         'NLP_SERVICE_ENDPOINT', 'https://nlp-api.lifelike.bio/v1/predict'
     )
diff --git a/appserver/neo4japp/blueprints/context.py b/appserver/neo4japp/blueprints/context.py
index 5460da633b..2df2d42ff3 100644
--- a/appserver/neo4japp/blueprints/context.py
+++ b/appserver/neo4japp/blueprints/context.py
@@ -5,6 +5,7 @@
 
 from neo4japp.schemas.context import ContextRelationshipRequestSchema
 from neo4japp.services.chat_gpt import ChatGPT
+from neo4japp.services.llm import LLM
 from neo4japp.utils.globals import get_current_username
 
 bp = Blueprint('chat-gpt-api', __name__, url_prefix='/explain')
@@ -50,3 +51,25 @@ def relationship(params):
             "result": choice.get('message').get('content').strip(),
             "query_params": create_params,
         }
+
+
+@bp.route('/relationship/graph', methods=['POST'])
+@use_args(ContextRelationshipRequestSchema)
+def relationship_graph(params):
+    entities = params.get('entities', [])
+    context = params.get('context')
+    options = params.get('options', {})
+    create_params = dict(
+        model="gpt-3.5-turbo",
+        query=(
+            'What is the relationship between '
+            + ', '.join(entities)
+            + (f', {context}' if context else '')
+            + '?'
+        ),
+        temperature=options.get('temperature', 0),
+        max_tokens=2000,
+        user=str(hash(get_current_username())),
+        timeout=60,
+    )
+    return LLM.graph_qa(**create_params)
diff --git a/appserver/neo4japp/services/llm.py b/appserver/neo4japp/services/llm.py
new file mode 100644
index 0000000000..5009ed5f61
--- /dev/null
+++ b/appserver/neo4japp/services/llm.py
@@ -0,0 +1,87 @@
+import json
+from http import HTTPStatus
+from typing import cast
+
+import requests
+from flask import current_app, Response, g
+
+from neo4japp.exceptions import ServerException
+from neo4japp.utils.globals import config
+from neo4japp.utils.transaction_id import transaction_id
+
+
+class LLM:
+    @classmethod
+    def request(cls, path, **request_args) -> Response:
+        host = config.get('LLM_HOST')
+        port = config.get('LLM_PORT')
+        host_port = f'{host}:{port}'
+        url = f'http://{host_port}{path}'
+        try:
+            request_args['url'] = url
+            resp = requests.request(**request_args)
+            excluded_headers = [
+                'content-encoding',
+                'content-length',
+                'transfer-encoding',
+                'connection',
+            ]
+            headers = [
+                (name, value)
+                for (name, value) in resp.raw.headers.items()
+                if name.lower() not in excluded_headers
+            ]
+        except Exception as e:
+            raise ServerException(
+                'An unexpected error occurred while connecting to service.',
+                fields={
+                    arg: request_args[arg]
+                    for arg in request_args
+                    if arg not in ['headers', 'cookies']
+                },
+            ) from e
+
+        # 500 should contain message from service so we try to include it
+        if resp.status_code == HTTPStatus.INTERNAL_SERVER_ERROR:
+            try:
+                decoded_error_message = json.loads(resp.content)['message']
+            except Exception as e:
+                # log and proceed so general error can be raised
+                current_app.logger.error(
+                    f'Could not process 500 error response from forwarded request.',
+                    exc_info=e,
+                )
+            else:
+                raise ServerException(
+                    'Service error',
+                    decoded_error_message,
+                    code=cast(HTTPStatus, resp.status_code),
+                )
+
+        # All errors including failure to parse internal error message
+        if 400 <= resp.status_code < 600:
+            raise ServerException(
+                'Unable to process request',
+                'An internal error of service occurred.',
+                code=cast(HTTPStatus, resp.status_code),
+            )
+
+        return Response(resp.content, resp.status_code, headers)
+
+    @classmethod
+    def graph_qa(cls, query, user, **data):
+        return cls.request(
+            '/graph',
+            data=json.dumps(
+                {
+                    'query': query,
+                    'transaction_id': getattr(g, 'transaction_id'),
+                    'user': user,
+                    'graph': {
+                        'database_type': 'arango',
+                    },
+                }
+            ),
+            method='POST',
+            headers={'Content-Type': 'application/json'},
+        )
diff --git a/client/src/app/drawing-tool/components/prompt/prompt.component.html b/client/src/app/drawing-tool/components/prompt/prompt.component.html
index b2308eaa5e..36eabc30b5 100644
--- a/client/src/app/drawing-tool/components/prompt/prompt.component.html
+++ b/client/src/app/drawing-tool/components/prompt/prompt.component.html
@@ -65,7 +65,9 @@
 <ng-container
   *ngIf="{
     explain: explain$ | async,
-    explanation: explanation$ | async
+    explanation: explanation$ | async,
+    graph_explanation: graphExplanation$ | async,
+    graph: graph$ | async
   } as state"
 >
   <button
@@ -80,30 +82,81 @@
       {{ state.explain ? 'Regenerate' : 'Generate' }} explanation
     </ng-template>
   </button>
-  <br />
-  <div class="card mt-2" style="white-space: pre-line" *ngIf="state.explanation?.value">
-    <div class="card-body p-3 bg-light" style="white-space: pre-wrap">
-      <span
-        style="float: right"
-        class="ml-2 mb-2"
-        (click)="copyToClipboard(state.explanation.value.result)"
-      >
-        <i class="fas fa-copy"></i>
-      </span>
-      <span
-        style="float: right"
-        class="ml-2 mb-2"
-        (click)="openInfo(state.explanation.value.query_params)"
-      >
-        <i class="fas fa-info-circle"></i>
-      </span>
-      {{ state.explanation.value.result }}
+  <ng-container *ngIf="state.explain">
+    <div class="card mt-2" style="white-space: pre-line">
+      <div class="card-body p-3 bg-light" style="white-space: pre-wrap">
+        <div>
+          ChatGPT
+          <span
+            style="float: right"
+            class="ml-2 mb-2"
+            (click)="copyToClipboard(state.explanation.value.result)"
+          >
+            <i class="fas fa-copy"></i>
+          </span>
+          <span
+            style="float: right"
+            class="ml-2"
+            *ngIf="state.explanation?.value?.query_params"
+            (click)="openInfo(state.explanation?.value?.query_params)"
+          >
+            <i class="fas fa-info-circle"></i>
+          </span>
+        </div>
+        <ng-container *ngIf="state.explanation?.loading; else settled_chat">
+          <i class="fa fa-spinner fa-spin mr-1"></i>
+        </ng-container>
+        <ng-template #settled_chat>
+          {{ state.explanation.value.result }}
+        </ng-template>
+      </div>
     </div>
-  </div>
+    <div class="card mt-2" style="white-space: pre-line">
+      <div class="card-body p-3 bg-light" style="white-space: pre-wrap">
+        <div>
+          ChatGPT over graph
+          <span
+            style="float: right"
+            class="ml-2 mb-2"
+            (click)="copyToClipboard(state.graph_explanation.value?.result?.result)"
+          >
+            <i class="fas fa-copy"></i>
+          </span>
+          <span
+            style="float: right"
+            class="ml-2 mb-2"
+            *ngIf="state.graph_explanation?.value?.result?.query"
+            (click)="openInfo({ query: state.graph_explanation?.value?.result?.query })"
+          >
+            <i class="fas fa-info-circle"></i>
+          </span>
+        </div>
+        <ng-container *ngIf="state.graph_explanation?.loading; else settled_graph">
+          <i class="fa fa-spinner fa-spin mr-1"></i>
+        </ng-container>
+        <ng-template #settled_graph>
+          {{ state.graph_explanation.value?.result?.result }}
+        </ng-template>
+        <app-vis-network
+          style="height: 200px; resize: vertical; overflow: hidden"
+          class="mt-1"
+          *ngIf="state.graph?.nodes?.length"
+          [data]="state.graph"
+          [options]="networkConfig"
+        ></app-vis-network>
+      </div>
+    </div>
+  </ng-container>
 
   <app-module-error
     class="d-block mt-4"
     *ngIf="state.explanation?.error as error"
     [error]="error"
   ></app-module-error>
+
+  <app-module-error
+    class="d-block mt-4"
+    *ngIf="state.graph_explanation?.error as error"
+    [error]="error"
+  ></app-module-error>
 </ng-container>
diff --git a/client/src/app/drawing-tool/components/prompt/prompt.component.ts b/client/src/app/drawing-tool/components/prompt/prompt.component.ts
index 802a608740..692f5ad478 100644
--- a/client/src/app/drawing-tool/components/prompt/prompt.component.ts
+++ b/client/src/app/drawing-tool/components/prompt/prompt.component.ts
@@ -19,8 +19,8 @@ import {
 } from 'app/playground/components/form/drawing-tool-prompt-form/drawing-tool-prompt-form.component';
 import { OpenPlaygroundParams } from 'app/playground/components/open-playground/open-playground.component';
 import { OpenFileProvider } from 'app/shared/providers/open-file/open-file.provider';
-import { ExplainService } from 'app/shared/services/explain.service';
 import { DropdownController } from 'app/shared/utils/dropdown.controller.factory';
+import { ExplainService, GraphChatGPTResponse } from 'app/shared/services/explain.service';
 import { openModal } from 'app/shared/utils/modals';
 import { PlaygroundComponent } from 'app/playground/components/playground.component';
 import { ChatgptResponseInfoModalComponent } from 'app/shared/components/chatgpt-response-info-modal/chatgpt-response-info-modal.component';
@@ -28,6 +28,7 @@ import { ChatGPTResponse } from 'app/enrichment/services/enrichment-visualisatio
 import { addStatus, PipeStatus } from 'app/shared/pipes/add-status.pipe';
 import { ClipboardService } from 'app/shared/services/clipboard.service';
 import { AuthenticationService } from 'app/auth/services/authentication.service';
+import { annotationTypesMap } from 'app/shared/annotation-styles';
 
 import { MapStoreService, setContext } from '../../services/map-store.service';
 
@@ -46,6 +47,44 @@ export class DrawingToolPromptComponent implements OnDestroy, OnChanges {
     readonly authService: AuthenticationService
   ) {}
 
+  networkConfig = {
+    interaction: {
+      hover: true,
+      multiselect: true,
+      selectConnectedEdges: false,
+    },
+    physics: {
+      enabled: true,
+      solver: 'barnesHut',
+    },
+    edges: {
+      font: {
+        size: 12,
+      },
+      length: 250,
+      widthConstraint: {
+        maximum: 90,
+      },
+    },
+    nodes: {
+      scaling: {
+        min: 25,
+        max: 50,
+        label: {
+          enabled: true,
+          min: 12,
+          max: 72,
+          maxVisible: 72,
+          drawThreshold: 5,
+        },
+      },
+      shape: 'box',
+      widthConstraint: {
+        maximum: 180,
+      },
+    },
+  };
+
   private readonly destroy$: Subject<void> = new Subject();
 
   @Input() entities!: Iterable<string>;
@@ -110,6 +149,38 @@ export class DrawingToolPromptComponent implements OnDestroy, OnChanges {
     )
   );
 
+  readonly graphExplanation$: Observable<PipeStatus<GraphChatGPTResponse>> = this.params$.pipe(
+    switchMap((params) =>
+      this.explain$.pipe(
+        map(() => params),
+        takeUntil(this.destroy$),
+        switchMap(({ entities, temperature, context }) =>
+          this.explainService
+            .relationshipGraph(entities, context, { temperature })
+            .pipe(addStatus())
+        ),
+        startWith(undefined)
+      )
+    )
+  );
+
+  readonly graph$ = this.graphExplanation$.pipe(
+    map((explanation) => explanation?.value?.graph),
+    map((graph) =>
+      graph
+        ? {
+            edges: graph.edges,
+            nodes: graph.nodes.map(({ eid, type, entityType, displayName }) => ({
+              id: eid,
+              label: displayName,
+              color: annotationTypesMap.get((type ?? entityType)?.toLowerCase())?.color,
+            })),
+          }
+        : undefined
+    ),
+    shareReplay({ bufferSize: 1, refCount: true })
+  );
+
   readonly playgroundParams$: Observable<OpenPlaygroundParams<DrawingToolPromptFormParams>> =
     combineLatest([this.params$, this.contexts$]).pipe(
       map(([{ temperature, entities, context }, contexts]) => ({
diff --git a/client/src/app/shared/directives/shareddirectives.module.ts b/client/src/app/shared/directives/shareddirectives.module.ts
index 52563751cf..7a7ba48335 100644
--- a/client/src/app/shared/directives/shareddirectives.module.ts
+++ b/client/src/app/shared/directives/shareddirectives.module.ts
@@ -33,6 +33,7 @@ import {
   InteractiveInterfaceHasPlaceholderDirective,
   ShowPlaceholderDirective,
 } from './placeholder.directive';
+import { VisNetworkDirective } from './vis-network.directive';
 
 const directives = [
   HasPlaceholderDirective,
@@ -43,6 +44,7 @@ const directives = [
   DebounceInputDirective,
   LinkWithoutHrefDirective,
   LinkWithHrefDirective,
+  VisNetworkDirective,
   FormInputDirective,
   AutoFocusDirective,
   InnerXMLDirective,
@@ -67,7 +69,7 @@ const directives = [
 
 @NgModule({
   imports: [],
-  declarations: [...directives],
+  declarations: [...directives, VisNetworkDirective],
   exports: [...directives],
 })
 export class SharedDirectivesModule {}
diff --git a/client/src/app/shared/directives/vis-network.directive.ts b/client/src/app/shared/directives/vis-network.directive.ts
new file mode 100644
index 0000000000..fc2234ff17
--- /dev/null
+++ b/client/src/app/shared/directives/vis-network.directive.ts
@@ -0,0 +1,414 @@
+import {
+  AfterViewInit,
+  Directive,
+  ElementRef,
+  Input,
+  OnChanges,
+  OnDestroy,
+  Output,
+  Renderer2,
+  SimpleChanges,
+} from '@angular/core';
+
+import {
+  BoundingBox,
+  ClusterOptions,
+  Data,
+  DirectionType,
+  EdgeOptions,
+  FitOptions,
+  FocusOptions,
+  IdType,
+  MoveToOptions,
+  Network,
+  NetworkEvents,
+  NodeOptions,
+  OpenClusterOptions,
+  Options,
+  Position,
+  SelectionOptions,
+} from 'vis-network';
+import { Observable, ReplaySubject } from 'rxjs';
+import { shareReplay, switchMap } from 'rxjs/operators';
+
+@Directive({
+  selector: 'app-vis-network[data][options],  [appVisNetwork][data][options]',
+})
+export class VisNetworkDirective implements Network, OnChanges, AfterViewInit, OnDestroy {
+  /**
+   * Versatile yet simple wrapper, implementing vis-network Network class as Angular component/directive.
+   * It allows to use vis-network in Angular templates in a declarative way.
+   * To avoid name conflicts all events has been prefixed with 'network' word.
+   *
+   * Examples of usage in templates:
+   * ```html
+   *    <app-vis-network [data]="data" [options]="options"></app-vis-network>
+   *    <div appVisNetwork [data]="data" [options]="options"></div>
+   *    <app-vis-network [data]="data" [options]="options" (networkClick)="click($event)"></app-vis-network>
+   *    <div appVisNetwork [data]="data" [options]="options" (networkClick)="click($event)"></div>
+   * ```
+   *
+   * Example of usage in component class:
+   * ```ts
+   *    @ViewChild(VisNetworkDirective) networkInstance: VisNetworkDirective;
+   *    this.networkInstance.network.fit();
+   * ```
+   *
+   * Example of usage in component class handling events outside Angular zone:
+   * ```ts
+   *    @ViewChild(VisNetworkDirective) networkInstance: VisNetworkDirective;
+   *    this.networkInstance.network.on('click', (params) => this.click(params));
+   * ```
+   *
+   * [See official vis-network documentation for details on methods and events.](https://visjs.github.io/vis-network/docs/network/)
+   */
+  constructor(private readonly element: ElementRef, private readonly renderer: Renderer2) {
+    // Set display to block to allow setting width and height
+    // TODO: This could be write to not overwrite diferent acceptable display values like inline-block
+    this.renderer.setStyle(element.nativeElement, 'display', 'block');
+  }
+
+  @Input() data!: Data;
+  @Input() options!: Options;
+  private _network: Network; // current vis-network instance, used to implement all methods
+  private _network$ = new ReplaySubject<Network>(1); // used to implement all events
+  // tslint:disable-next-line:no-output-rename
+  @Output('network') network$ = this._network$.asObservable();
+
+  get network(): Network {
+    return this._network;
+  }
+
+  set network(network: Network) {
+    this._network?.destroy();
+    this._network = network;
+    this._network$.next(network);
+  }
+
+  // region Network Events proxies
+  /**
+   * WARNING: These proxies will run in angular zone, so they will trigger change detection.
+   * If you want to avoid this, use VisNetworkDirective.on('eventName', callback) instead.
+   */
+  @Output() readonly networkClick = this.networkEventToEventEmitter('click');
+  @Output() readonly networkDoubleClick = this.networkEventToEventEmitter('doubleClick');
+  @Output() readonly networkOnContext = this.networkEventToEventEmitter('oncontext');
+  @Output() readonly networkHold = this.networkEventToEventEmitter('hold');
+  @Output() readonly networkRelease = this.networkEventToEventEmitter('release');
+  @Output() readonly networkSelect = this.networkEventToEventEmitter('select');
+  @Output() readonly networkSelectNode = this.networkEventToEventEmitter('selectNode');
+  @Output() readonly networkSelectEdge = this.networkEventToEventEmitter('selectEdge');
+  @Output() readonly networkDeselectNode = this.networkEventToEventEmitter('deselectNode');
+  @Output() readonly networkDeselectEdge = this.networkEventToEventEmitter('deselectEdge');
+  @Output() readonly networkDragStart = this.networkEventToEventEmitter('dragStart');
+  @Output() readonly networkDragging = this.networkEventToEventEmitter('dragging');
+  @Output() readonly networkDragEnd = this.networkEventToEventEmitter('dragEnd');
+  @Output() readonly networkControlNodeDragging =
+    this.networkEventToEventEmitter('controlNodeDragging');
+  @Output() readonly networkControlNodeDragEnd =
+    this.networkEventToEventEmitter('controlNodeDragEnd');
+  @Output() readonly networkHoverNode = this.networkEventToEventEmitter('hoverNode');
+  @Output() readonly networkBlurNode = this.networkEventToEventEmitter('blurNode');
+  @Output() readonly networkHoverEdge = this.networkEventToEventEmitter('hoverEdge');
+  @Output() readonly networkBlurEdge = this.networkEventToEventEmitter('blurEdge');
+  @Output() readonly networkZoom = this.networkEventToEventEmitter('zoom');
+  @Output() readonly networkShowPopup = this.networkEventToEventEmitter('showPopup');
+  @Output() readonly networkHidePopup = this.networkEventToEventEmitter('hidePopup');
+  @Output() readonly networkStartStabilizing = this.networkEventToEventEmitter('startStabilizing');
+  @Output() readonly networkStabilizationProgress =
+    this.networkEventToEventEmitter('stabilizationProgress');
+  @Output() readonly networkStabilizationIterationsDone = this.networkEventToEventEmitter(
+    'stabilizationIterationsDone'
+  );
+  @Output() readonly networkStabilized = this.networkEventToEventEmitter('stabilized');
+  @Output() readonly networkResize = this.networkEventToEventEmitter('resize');
+  @Output() readonly networkInitRedraw = this.networkEventToEventEmitter('initRedraw');
+  @Output() readonly networkBeforeDrawing = this.networkEventToEventEmitter('beforeDrawing');
+  @Output() readonly networkAfterDrawing = this.networkEventToEventEmitter('afterDrawing');
+  @Output() readonly networkAnimationFinished =
+    this.networkEventToEventEmitter('animationFinished');
+  @Output() readonly networkConfigChange = this.networkEventToEventEmitter('configChange');
+
+  private networkEventToEventEmitter(eventName: NetworkEvents) {
+    return this._network$.pipe(
+      switchMap(
+        (network) =>
+          new Observable<any>((subscriber) => {
+            const callback = (e: any) => subscriber.next(e);
+            network.on(eventName, callback);
+
+            return function unsubscribe() {
+              network.off(eventName, callback);
+            };
+          })
+      ),
+      shareReplay({ bufferSize: 1, refCount: true })
+    );
+  }
+  // endregion
+
+  // region Lifecycle
+  ngOnChanges({ data, options }: SimpleChanges) {
+    if (data && !data.firstChange) {
+      // initial value is set in ngAfterViewInit
+      this._network.setData(data.currentValue);
+    }
+    if (options && !options.firstChange) {
+      // initial value is set in ngAfterViewInit
+      this._network.setOptions(options.currentValue);
+    }
+  }
+
+  ngAfterViewInit() {
+    this._network = new Network(this.element.nativeElement, this.data, this.options);
+  }
+
+  ngOnDestroy() {
+    this._network?.destroy();
+  }
+  // endregion
+
+  // region Network methods proxy
+  DOMtoCanvas(position: Position): Position {
+    return this._network.DOMtoCanvas(position);
+  }
+
+  addEdgeMode(): void {
+    this._network.addEdgeMode();
+  }
+
+  addNodeMode(): void {
+    this._network.addNodeMode();
+  }
+
+  canvasToDOM(position: Position): Position {
+    return this._network.canvasToDOM(position);
+  }
+
+  cluster(options?: ClusterOptions): void {
+    this._network.cluster(options);
+  }
+
+  clusterByConnection(nodeId: string, options?: ClusterOptions): void {
+    this._network.clusterByConnection(nodeId, options);
+  }
+
+  clusterByHubsize(hubsize?: number, options?: ClusterOptions): void {
+    this._network.clusterByHubsize(hubsize, options);
+  }
+
+  clusterOutliers(options?: ClusterOptions): void {
+    this._network.clusterOutliers(options);
+  }
+
+  deleteSelected(): void {
+    this._network.deleteSelected();
+  }
+
+  destroy(): void {
+    this._network.destroy();
+  }
+
+  disableEditMode(): void {
+    this._network.disableEditMode();
+  }
+
+  editEdgeMode(): void {
+    this._network.editEdgeMode();
+  }
+
+  editNode(): void {
+    this._network.editNode();
+  }
+
+  enableEditMode(): void {
+    this._network.enableEditMode();
+  }
+
+  findNode(nodeId: IdType): IdType[] {
+    return this._network.findNode(nodeId);
+  }
+
+  fit(options?: FitOptions): void {
+    this._network.fit(options);
+  }
+
+  focus(nodeId: IdType, options?: FocusOptions): void {
+    this._network.focus(nodeId, options);
+  }
+
+  getBaseEdge(clusteredEdgeId: IdType): IdType {
+    return this._network.getBaseEdge(clusteredEdgeId);
+  }
+
+  getBaseEdges(clusteredEdgeId: IdType): IdType[] {
+    return this._network.getBaseEdges(clusteredEdgeId);
+  }
+
+  getBoundingBox(nodeId: IdType): BoundingBox {
+    return this._network.getBoundingBox(nodeId);
+  }
+
+  getClusteredEdges(baseEdgeId: IdType): IdType[] {
+    return this._network.getClusteredEdges(baseEdgeId);
+  }
+
+  getConnectedEdges(nodeId: IdType): IdType[] {
+    return this._network.getConnectedEdges(nodeId);
+  }
+
+  getConnectedNodes(
+    nodeOrEdgeId: IdType,
+    direction?: DirectionType
+  ):
+    | IdType[]
+    | Array<{
+        fromId: IdType;
+        toId: IdType;
+      }> {
+    return this._network.getConnectedNodes(nodeOrEdgeId, direction);
+  }
+
+  getEdgeAt(position: Position): IdType {
+    return this._network.getEdgeAt(position);
+  }
+
+  getNodeAt(position: Position): IdType {
+    return this._network.getNodeAt(position);
+  }
+
+  getNodesInCluster(clusterNodeId: IdType): IdType[] {
+    return this._network.getNodesInCluster(clusterNodeId);
+  }
+
+  getOptionsFromConfigurator(): any {
+    return this._network.getOptionsFromConfigurator();
+  }
+
+  getPosition(nodeId: IdType): Position {
+    return this._network.getPosition(nodeId);
+  }
+
+  getPositions(nodeIds?: IdType[] | IdType): { [p: string]: Position } {
+    return this._network.getPositions(nodeIds);
+  }
+
+  getScale(): number {
+    return this._network.getScale();
+  }
+
+  getSeed(): number | string {
+    return this._network.getSeed();
+  }
+
+  getSelectedEdges(): IdType[] {
+    return this._network.getSelectedEdges();
+  }
+
+  getSelectedNodes(): IdType[] {
+    return this._network.getSelectedNodes();
+  }
+
+  getSelection(): { nodes: IdType[]; edges: IdType[] } {
+    return this._network.getSelection();
+  }
+
+  getViewPosition(): Position {
+    return this._network.getViewPosition();
+  }
+
+  isCluster(nodeId: IdType): boolean {
+    return this._network.isCluster(nodeId);
+  }
+
+  moveNode(nodeId: IdType, x: number, y: number): void {
+    this._network.moveNode(nodeId, x, y);
+  }
+
+  moveTo(options: MoveToOptions): void {
+    this._network.moveTo(options);
+  }
+
+  off(eventName: NetworkEvents, callback?: (params?: any) => void): void {
+    this._network.off(eventName, callback);
+  }
+
+  on(eventName: NetworkEvents, callback: (params?: any) => void): void {
+    this._network.on(eventName, callback);
+  }
+
+  once(eventName: NetworkEvents, callback: (params?: any) => void): void {
+    this._network.once(eventName, callback);
+  }
+
+  openCluster(nodeId: IdType, options?: OpenClusterOptions): void {
+    this._network.openCluster(nodeId, options);
+  }
+
+  redraw(): void {
+    this._network.redraw();
+  }
+
+  releaseNode(): void {
+    this._network.releaseNode();
+  }
+
+  selectEdges(edgeIds: IdType[]): void {
+    this._network.selectEdges(edgeIds);
+  }
+
+  selectNodes(nodeIds: IdType[], highlightEdges?: boolean): void {
+    this._network.selectNodes(nodeIds, highlightEdges);
+  }
+
+  setData(data: Data): void {
+    this._network.setData(data);
+  }
+
+  setOptions(options: Options): void {
+    this._network.setOptions(options);
+  }
+
+  setSelection(
+    selection: {
+      nodes?: IdType[];
+      edges?: IdType[];
+    },
+    options?: SelectionOptions
+  ): void {
+    this._network.setSelection(selection, options);
+  }
+
+  setSize(width: string, height: string): void {
+    this._network.setSize(width, height);
+  }
+
+  stabilize(iterations?: number): void {
+    this._network.stabilize(iterations);
+  }
+
+  startSimulation(): void {
+    this._network.startSimulation();
+  }
+
+  stopSimulation(): void {
+    this._network.stopSimulation();
+  }
+
+  storePositions(): void {
+    this._network.storePositions();
+  }
+
+  unselectAll(): void {
+    this._network.unselectAll();
+  }
+
+  updateClusteredNode(clusteredNodeId: IdType, options?: NodeOptions): void {
+    this._network.updateClusteredNode(clusteredNodeId, options);
+  }
+
+  updateEdge(startEdgeId: IdType, options?: EdgeOptions): void {
+    this._network.updateEdge(startEdgeId, options);
+  }
+  // endregion
+}
diff --git a/client/src/app/shared/services/explain.service.ts b/client/src/app/shared/services/explain.service.ts
index 3065b9db5c..35dbef7f8a 100644
--- a/client/src/app/shared/services/explain.service.ts
+++ b/client/src/app/shared/services/explain.service.ts
@@ -2,7 +2,7 @@ import { Injectable } from '@angular/core';
 import { HttpClient } from '@angular/common/http';
 
 import { Observable } from 'rxjs';
-import { map } from 'rxjs/operators';
+import { map, tap } from 'rxjs/operators';
 
 import { SingleResult } from 'app/shared/schemas/common';
 import { ChatGPTResponse } from 'app/enrichment/services/enrichment-visualisation.service';
@@ -36,6 +36,23 @@ export interface ChatGPTModel {
   root: string;
 }
 
+export interface GraphChatGPTResponse {
+  graph: {
+    nodes: {
+      eid: string;
+      displayName: string;
+      type: string;
+      entityType: string;
+    }[];
+    edges: {
+      from: string;
+      to: string;
+      label: string;
+    }[];
+  };
+  response: ChatGPTResponse;
+}
+
 @Injectable({ providedIn: 'root' })
 export class ExplainService {
   endpoint = '/api/explain/';
@@ -53,4 +70,18 @@ export class ExplainService {
       options,
     });
   }
+
+  relationshipGraph(
+    entities: Iterable<string>,
+    context?: string,
+    options: ExplainRelationshipOptions = {}
+  ): Observable<GraphChatGPTResponse> {
+    return this.http
+      .post<GraphChatGPTResponse>(this.endpoint + 'relationship/graph', {
+        entities: Array.from(entities),
+        context,
+        options,
+      })
+      .pipe(tap((response) => console.log(response)));
+  }
 }
diff --git a/docker-compose.override.yml b/docker-compose.override.yml
index 2df05883fb..f516322a51 100644
--- a/docker-compose.override.yml
+++ b/docker-compose.override.yml
@@ -33,6 +33,9 @@ x-appserver: &appserver
     # StatisticalEnrichment
     - SE_HOST=statistical-enrichment
     - SE_PORT=5010
+    # LLM
+    - LLM_HOST=llm
+    - LLM_PORT=5000
     # Other
     - DOMAIN=http://localhost:4200
     - LMDB_HOME_FOLDER=/home/n4j/neo4japp/services/annotations/lmdb/
@@ -82,6 +85,41 @@ services:
     networks:
       - frontend
 
+  llm:
+    container_name: llm
+    build:
+      context: ./llm
+      target: dev
+    entrypoint: flask run --host 0.0.0.0
+    ports:
+      - "5020:5000"
+    env_file:
+      - azure-secrets.env
+    environment:
+      # Flask
+      FLASK_APP: llmlib
+      FLASK_APP_CONFIG: Development
+      FLASK_DEBUG: 1
+      FLASK_ENV: development
+      # Arango
+      ARANGO_HOST: http://arangodb:8529
+      ARANGO_USERNAME: root
+      ARANGO_PASSWORD: password
+      ARANGO_DB_NAME: lifelike
+      # Redis
+      REDIS_HOST: redis
+      REDIS_PORT: 6379
+      REDIS_USERNAME: default
+      REDIS_PASSWORD: password
+      REDIS_SSL: false
+    volumes:
+      - ./llm:/home/user
+    networks:
+      - backend
+    depends_on:
+      - arangodb
+      - redis
+
   statistical-enrichment:
     container_name: statistical-enrichment
     build: ./statistical-enrichment
diff --git a/docker-compose.yml b/docker-compose.yml
index 82f61fb645..0bbe5139bf 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -16,5 +16,8 @@ services:
   cache-invalidator:
     image: ${REGISTRY:-lifelike.azurecr.io}/kg-cache-invalidator:${GITHUB_HASH:-latest}
 
+  llm:
+    image: ${REGISTRY:-lifelike.azurecr.io}/kg-llm:${GITHUB_HASH:-latest}
+
   statistical-enrichment:
     image: ${REGISTRY:-lifelike.azurecr.io}/kg-statistical-enrichment:${GITHUB_HASH:-latest}
diff --git a/llm/.githooks/pre-commit b/llm/.githooks/pre-commit
new file mode 100755
index 0000000000..984588bbda
--- /dev/null
+++ b/llm/.githooks/pre-commit
@@ -0,0 +1,27 @@
+#!/bin/sh
+
+# Path from shell to folder containing this file
+__dir__="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
+
+# Runs linting on only files that are staged
+staged_files=$(getStaged llmlib '\.(py)$')
+
+# shellcheck disable=SC2236
+if [[ -n $staged_files ]];
+then
+  (
+    echo "> 🌽 Running flake8..."
+    echo ${staged_files} | \
+      xargs docker run --rm \
+      -v $(pwd)/llmlib:/apps \
+      alpine/flake8:6.0.0
+  ) && (
+    echo "> 🐍 Running pycodestyle..."
+    echo ${staged_files} | \
+      xargs docker run --rm \
+      -v $(pwd)/llmlib:/data \
+      cytopia/pycodestyle:2
+  )
+else
+  echo "> Skipping python files check for llm..."
+fi
diff --git a/llm/.gitignore b/llm/.gitignore
new file mode 100644
index 0000000000..84ded39530
--- /dev/null
+++ b/llm/.gitignore
@@ -0,0 +1,6 @@
+**/.ipynb_checkpoints
+**/.Trash-0
+**/.jupyter
+**/*.egg-info
+**/chroma_db_oai
+**/.pytest_cache
diff --git a/llm/Dockerfile b/llm/Dockerfile
new file mode 100644
index 0000000000..5d8cecab4c
--- /dev/null
+++ b/llm/Dockerfile
@@ -0,0 +1,61 @@
+FROM python:3.11 as base
+LABEL app=kg-prototypes
+
+ENV _USER user
+ENV _HOME /home/$_USER
+ENV UID 1000
+ENV GID 1000
+
+# Root acces only if in ***ARANGO_USERNAME*** group
+RUN echo "auth required pam_wheel.so" >> /etc/pam.d/su
+
+# Install dependencies
+RUN pip install pipenv==2026.0.3
+
+# User and group creation
+RUN addgroup --system wheel && \
+    groupadd -g $GID $_USER && \
+    useradd -u $UID -g $GID -G wheel --create-home --shell /bin/bash $_USER
+
+WORKDIR $_HOME
+
+# Copy Pipfiles and install dependencies FIRST to better apply Docker layer cache
+COPY --chown=$UID:$GID Pipfile .
+
+FROM base as lock
+
+COPY --chown=$UID:$GID . .
+
+CMD [ "pipenv", "lock" ]
+
+FROM base as build
+
+COPY --chown=$UID:$GID Pipfile.lock .
+
+ENV FLASK_APP=llm
+
+FROM build as dev
+
+# Copy files first so pipenv can link to editable package
+COPY --chown=$UID:$GID . .
+
+# Install dev dependencies
+# (NOTE: cannot run after `pipenv install` - https://github.com/pypa/pipenv/issues/4435)
+RUN pipenv install --dev --deploy --system
+
+COPY --chown=$UID:$GID . .
+
+USER $_USER
+
+# Jupyter Notebook
+EXPOSE 8888
+
+FROM build as prod
+
+RUN pipenv install --deploy --system
+
+COPY --chown=$UID:$GID . .
+
+USER $_USER
+
+CMD [ "bin/startup.sh" ]
diff --git a/llm/Pipfile b/llm/Pipfile
new file mode 100644
index 0000000000..22c212923d
--- /dev/null
+++ b/llm/Pipfile
@@ -0,0 +1,35 @@
+[[source]]
+name = "pypi"
+url = "https://pypi.org/simple"
+verify_ssl = true
+
+[dev-packages]
+pytest = "*"
+mypy = "==1.5.1"
+pycodestyle = "==2.11.0"
+jupyterlab = "*"
+anyio = "*"
+ipycytoscape = "*"
+ipywidgets = "*"
+networkx = "*"
+adbnx-adapter = "*"
+llmlib = {file = ".", editable = true}
+tiktoken = "*"
+
+[packages]
+flask-marshmallow = "==0.15.0"
+Flask = "==2.3.3"
+gunicorn = "==21.2.0"
+python-arango = "==7.6.2"
+openai = "==0.28.0"
+langchain = "==0.0.296"
+redis = "==5.0.0"
+cachetools = "==5.3.1"
+webargs = "*"
+notebook = ">=7.0"
+timeflake = "*"
+chromadb = "*"
+neo4j = "*"
+
+[requires]
+python_version = "3.11"
diff --git a/llm/Pipfile.lock b/llm/Pipfile.lock
new file mode 100644
index 0000000000..823a86770f
--- /dev/null
+++ b/llm/Pipfile.lock
@@ -0,0 +1,4474 @@
+{
+    "_meta": {
+        "hash": {
+            "sha256": "1057a684dff40a1507710ea54d2eb6f9bbbbd246db817be1eccb62c84c1ded1d"
+        },
+        "pipfile-spec": 6,
+        "requires": {
+            "python_version": "3.11"
+        },
+        "sources": [
+            {
+                "name": "pypi",
+                "url": "https://pypi.org/simple",
+                "verify_ssl": true
+            }
+        ]
+    },
+    "default": {
+        "aiohttp": {
+            "hashes": [
+                "sha256:002f23e6ea8d3dd8d149e569fd580c999232b5fbc601c48d55398fbc2e582e8c",
+                "sha256:01770d8c04bd8db568abb636c1fdd4f7140b284b8b3e0b4584f070180c1e5c62",
+                "sha256:0912ed87fee967940aacc5306d3aa8ba3a459fcd12add0b407081fbefc931e53",
+                "sha256:0cccd1de239afa866e4ce5c789b3032442f19c261c7d8a01183fd956b1935349",
+                "sha256:0fa375b3d34e71ccccf172cab401cd94a72de7a8cc01847a7b3386204093bb47",
+                "sha256:13da35c9ceb847732bf5c6c5781dcf4780e14392e5d3b3c689f6d22f8e15ae31",
+                "sha256:14cd52ccf40006c7a6cd34a0f8663734e5363fd981807173faf3a017e202fec9",
+                "sha256:16d330b3b9db87c3883e565340d292638a878236418b23cc8b9b11a054aaa887",
+                "sha256:1bed815f3dc3d915c5c1e556c397c8667826fbc1b935d95b0ad680787896a358",
+                "sha256:1d84166673694841d8953f0a8d0c90e1087739d24632fe86b1a08819168b4566",
+                "sha256:1f13f60d78224f0dace220d8ab4ef1dbc37115eeeab8c06804fec11bec2bbd07",
+                "sha256:229852e147f44da0241954fc6cb910ba074e597f06789c867cb7fb0621e0ba7a",
+                "sha256:253bf92b744b3170eb4c4ca2fa58f9c4b87aeb1df42f71d4e78815e6e8b73c9e",
+                "sha256:255ba9d6d5ff1a382bb9a578cd563605aa69bec845680e21c44afc2670607a95",
+                "sha256:2817b2f66ca82ee699acd90e05c95e79bbf1dc986abb62b61ec8aaf851e81c93",
+                "sha256:2b8d4e166e600dcfbff51919c7a3789ff6ca8b3ecce16e1d9c96d95dd569eb4c",
+                "sha256:2d5b785c792802e7b275c420d84f3397668e9d49ab1cb52bd916b3b3ffcf09ad",
+                "sha256:3161ce82ab85acd267c8f4b14aa226047a6bee1e4e6adb74b798bd42c6ae1f80",
+                "sha256:33164093be11fcef3ce2571a0dccd9041c9a93fa3bde86569d7b03120d276c6f",
+                "sha256:39a312d0e991690ccc1a61f1e9e42daa519dcc34ad03eb6f826d94c1190190dd",
+                "sha256:3b2ab182fc28e7a81f6c70bfbd829045d9480063f5ab06f6e601a3eddbbd49a0",
+                "sha256:3c68330a59506254b556b99a91857428cab98b2f84061260a67865f7f52899f5",
+                "sha256:3f0e27e5b733803333bb2371249f41cf42bae8884863e8e8965ec69bebe53132",
+                "sha256:3f5c7ce535a1d2429a634310e308fb7d718905487257060e5d4598e29dc17f0b",
+                "sha256:3fd194939b1f764d6bb05490987bfe104287bbf51b8d862261ccf66f48fb4096",
+                "sha256:41bdc2ba359032e36c0e9de5a3bd00d6fb7ea558a6ce6b70acedf0da86458321",
+                "sha256:41d55fc043954cddbbd82503d9cc3f4814a40bcef30b3569bc7b5e34130718c1",
+                "sha256:42c89579f82e49db436b69c938ab3e1559e5a4409eb8639eb4143989bc390f2f",
+                "sha256:45ad816b2c8e3b60b510f30dbd37fe74fd4a772248a52bb021f6fd65dff809b6",
+                "sha256:4ac39027011414dbd3d87f7edb31680e1f430834c8cef029f11c66dad0670aa5",
+                "sha256:4d4cbe4ffa9d05f46a28252efc5941e0462792930caa370a6efaf491f412bc66",
+                "sha256:4fcf3eabd3fd1a5e6092d1242295fa37d0354b2eb2077e6eb670accad78e40e1",
+                "sha256:5d791245a894be071d5ab04bbb4850534261a7d4fd363b094a7b9963e8cdbd31",
+                "sha256:6c43ecfef7deaf0617cee936836518e7424ee12cb709883f2c9a1adda63cc460",
+                "sha256:6c5f938d199a6fdbdc10bbb9447496561c3a9a565b43be564648d81e1102ac22",
+                "sha256:6e2f9cc8e5328f829f6e1fb74a0a3a939b14e67e80832975e01929e320386b34",
+                "sha256:713103a8bdde61d13490adf47171a1039fd880113981e55401a0f7b42c37d071",
+                "sha256:71783b0b6455ac8f34b5ec99d83e686892c50498d5d00b8e56d47f41b38fbe04",
+                "sha256:76b36b3124f0223903609944a3c8bf28a599b2cc0ce0be60b45211c8e9be97f8",
+                "sha256:7bc88fc494b1f0311d67f29fee6fd636606f4697e8cc793a2d912ac5b19aa38d",
+                "sha256:7ee912f7e78287516df155f69da575a0ba33b02dd7c1d6614dbc9463f43066e3",
+                "sha256:86f20cee0f0a317c76573b627b954c412ea766d6ada1a9fcf1b805763ae7feeb",
+                "sha256:89341b2c19fb5eac30c341133ae2cc3544d40d9b1892749cdd25892bbc6ac951",
+                "sha256:8a9b5a0606faca4f6cc0d338359d6fa137104c337f489cd135bb7fbdbccb1e39",
+                "sha256:8d399dade330c53b4106160f75f55407e9ae7505263ea86f2ccca6bfcbdb4921",
+                "sha256:8e31e9db1bee8b4f407b77fd2507337a0a80665ad7b6c749d08df595d88f1cf5",
+                "sha256:90c72ebb7cb3a08a7f40061079817133f502a160561d0675b0a6adf231382c92",
+                "sha256:918810ef188f84152af6b938254911055a72e0f935b5fbc4c1a4ed0b0584aed1",
+                "sha256:93c15c8e48e5e7b89d5cb4613479d144fda8344e2d886cf694fd36db4cc86865",
+                "sha256:96603a562b546632441926cd1293cfcb5b69f0b4159e6077f7c7dbdfb686af4d",
+                "sha256:99c5ac4ad492b4a19fc132306cd57075c28446ec2ed970973bbf036bcda1bcc6",
+                "sha256:9c19b26acdd08dd239e0d3669a3dddafd600902e37881f13fbd8a53943079dbc",
+                "sha256:9de50a199b7710fa2904be5a4a9b51af587ab24c8e540a7243ab737b45844543",
+                "sha256:9e2ee0ac5a1f5c7dd3197de309adfb99ac4617ff02b0603fd1e65b07dc772e4b",
+                "sha256:a2ece4af1f3c967a4390c284797ab595a9f1bc1130ef8b01828915a05a6ae684",
+                "sha256:a3628b6c7b880b181a3ae0a0683698513874df63783fd89de99b7b7539e3e8a8",
+                "sha256:ad1407db8f2f49329729564f71685557157bfa42b48f4b93e53721a16eb813ed",
+                "sha256:b04691bc6601ef47c88f0255043df6f570ada1a9ebef99c34bd0b72866c217ae",
+                "sha256:b0cf2a4501bff9330a8a5248b4ce951851e415bdcce9dc158e76cfd55e15085c",
+                "sha256:b2fe42e523be344124c6c8ef32a011444e869dc5f883c591ed87f84339de5976",
+                "sha256:b30e963f9e0d52c28f284d554a9469af073030030cef8693106d918b2ca92f54",
+                "sha256:bb54c54510e47a8c7c8e63454a6acc817519337b2b78606c4e840871a3e15349",
+                "sha256:bd111d7fc5591ddf377a408ed9067045259ff2770f37e2d94e6478d0f3fc0c17",
+                "sha256:bdf70bfe5a1414ba9afb9d49f0c912dc524cf60141102f3a11143ba3d291870f",
+                "sha256:ca80e1b90a05a4f476547f904992ae81eda5c2c85c66ee4195bb8f9c5fb47f28",
+                "sha256:caf486ac1e689dda3502567eb89ffe02876546599bbf915ec94b1fa424eeffd4",
+                "sha256:ccc360e87341ad47c777f5723f68adbb52b37ab450c8bc3ca9ca1f3e849e5fe2",
+                "sha256:d25036d161c4fe2225d1abff2bd52c34ed0b1099f02c208cd34d8c05729882f0",
+                "sha256:d52d5dc7c6682b720280f9d9db41d36ebe4791622c842e258c9206232251ab2b",
+                "sha256:d67f8baed00870aa390ea2590798766256f31dc5ed3ecc737debb6e97e2ede78",
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+            ],
+            "index": "pypi",
+            "markers": "python_version >= '3.8'",
+            "version": "==0.5.1"
+        },
+        "tinycss2": {
+            "hashes": [
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+            "markers": "python_version >= '3.7'",
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+        "tornado": {
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+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==6.3.3"
+        },
+        "traitlets": {
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+                "sha256:baf991e61542da48fe8aef8b779a9ea0aa38d8a54166ee250d5af5ecf4486619"
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+            "markers": "python_version >= '3.8'",
+            "version": "==5.13.0"
+        },
+        "types-python-dateutil": {
+            "hashes": [
+                "sha256:1f4f10ac98bb8b16ade9dbee3518d9ace017821d94b057a425b069f834737f4b",
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+            ],
+            "version": "==2.8.19.14"
+        },
+        "typing-extensions": {
+            "hashes": [
+                "sha256:8f92fc8806f9a6b641eaa5318da32b44d401efaac0f6678c9bc448ba3605faa0",
+                "sha256:df8e4339e9cb77357558cbdbceca33c303714cf861d1eef15e1070055ae8b7ef"
+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==4.8.0"
+        },
+        "uri-template": {
+            "hashes": [
+                "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7",
+                "sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363"
+            ],
+            "version": "==1.3.0"
+        },
+        "urllib3": {
+            "hashes": [
+                "sha256:34b97092d7e0a3a8cf7cd10e386f401b3737364026c45e622aa02903dffe0f07",
+                "sha256:f8ecc1bba5667413457c529ab955bf8c67b45db799d159066261719e328580a0"
+            ],
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4, 3.5'",
+            "version": "==1.26.18"
+        },
+        "wcwidth": {
+            "hashes": [
+                "sha256:390c7454101092a6a5e43baad8f83de615463af459201709556b6e4b1c861f97",
+                "sha256:aec5179002dd0f0d40c456026e74a729661c9d468e1ed64405e3a6c2176ca36f"
+            ],
+            "version": "==0.2.10"
+        },
+        "webcolors": {
+            "hashes": [
+                "sha256:29bc7e8752c0a1bd4a1f03c14d6e6a72e93d82193738fa860cbff59d0fcc11bf",
+                "sha256:c225b674c83fa923be93d235330ce0300373d02885cef23238813b0d5668304a"
+            ],
+            "version": "==1.13"
+        },
+        "webencodings": {
+            "hashes": [
+                "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78",
+                "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"
+            ],
+            "version": "==0.5.1"
+        },
+        "websocket-client": {
+            "hashes": [
+                "sha256:084072e0a7f5f347ef2ac3d8698a5e0b4ffbfcab607628cadabc650fc9a83a24",
+                "sha256:b3324019b3c28572086c4a319f91d1dcd44e6e11cd340232978c684a7650d0df"
+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==1.6.4"
+        },
+        "widgetsnbextension": {
+            "hashes": [
+                "sha256:3c1f5e46dc1166dfd40a42d685e6a51396fd34ff878742a3e47c6f0cc4a2a385",
+                "sha256:91452ca8445beb805792f206e560c1769284267a30ceb1cec9f5bcc887d15175"
+            ],
+            "markers": "python_version >= '3.7'",
+            "version": "==4.0.9"
+        },
+        "zipp": {
+            "hashes": [
+                "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31",
+                "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"
+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==3.17.0"
+        }
+    }
+}
diff --git a/llm/bin/dev.sh b/llm/bin/dev.sh
new file mode 100755
index 0000000000..0493520bdd
--- /dev/null
+++ b/llm/bin/dev.sh
@@ -0,0 +1,6 @@
+#!/usr/bin/env bash
+
+set -o errexit                  # exit on command failure; use <cmd> || true to allow for exception
+set -o nounset                  # exit when script tries to use undeclared variables
+
+python -m jupyterlab --ip=0.0.0.0 --allow-***ARANGO_USERNAME*** --no-browser --port 8888
diff --git a/llm/bin/startup.sh b/llm/bin/startup.sh
new file mode 100755
index 0000000000..510a631fca
--- /dev/null
+++ b/llm/bin/startup.sh
@@ -0,0 +1,12 @@
+#!/usr/bin/env bash
+
+set -o errexit                  # exit on command failure; use <cmd> || true to allow for exception
+set -o nounset                  # exit when script tries to use undeclared variables
+
+if [ "${FLASK_ENV}" = "development" ] && [ "${FLASK_APP_CONFIG}" = "Development" ]; then
+    flask run --host 0.0.0.0
+elif [ "${FLASK_APP_CONFIG}" = "Production" ] || [ "${FLASK_APP_CONFIG}" = "Staging" ] || [ "${FLASK_APP_CONFIG}" = "QA" ]; then
+    gunicorn -b 0.0.0.0:5000 --workers=2 --max-requests=200 "llmlib.app:app" --timeout 1200
+else
+    echo "No environment setup for ${FLASK_ENV}"
+fi
diff --git a/llm/develop/Using query chain.ipynb b/llm/develop/Using query chain.ipynb
new file mode 100644
index 0000000000..441dcf3da6
--- /dev/null
+++ b/llm/develop/Using query chain.ipynb	
@@ -0,0 +1,1208 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6fda1acd-9514-4db5-ba8e-68255beb4c51",
+   "metadata": {},
+   "source": [
+    "# Init"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d349fffb-0042-4a5a-8984-d9c937be660e",
+   "metadata": {
+    "collapsed": false,
+    "is_executing": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "98bb8a3a-1acb-4fab-a8dd-c7671b684fab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from app import app\n",
+    "context = app.app_context()\n",
+    "context.push()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77a6dda5-ebc3-4d0a-8c22-62295460d871",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "603eded2-cac8-4cf6-9a7f-9294369e96cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import yaml\n",
+    "from utils import formatters # Provide jupyter output formating"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd918ef6-ec05-420f-8dc4-3a84e50de805",
+   "metadata": {},
+   "source": [
+    "## Inputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d0ad8eda-d372-4002-b609-42899582eb2a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query=\"What is the relationship between Zn2+ and glycolate?\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "877af485-bd94-4352-a511-d25d3e561b87",
+   "metadata": {},
+   "source": [
+    "## Initial check\n",
+    "\n",
+    "Current response:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f791641-1009-4fb6-b1ea-26521b8dddd2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import openai\n",
+    "response = openai.ChatCompletion.create(\n",
+    "    messages=[\n",
+    "        { \"role\": \"user\", \"content\": query }\n",
+    "    ],\n",
+    "    model=\"gpt-3.5-turbo\",\n",
+    "    temperature=0\n",
+    ")\n",
+    "response"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c0298d0-b147-4971-9606-d871307ebe56",
+   "metadata": {},
+   "source": [
+    "# Extract core terms"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ac517bb-5e7c-4562-9e49-25dbd2658122",
+   "metadata": {},
+   "source": [
+    "## Init model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7c93291-a342-485f-950e-97d12e528bfc",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "llm = ChatOpenAI(temperature=0)\n",
+    "llm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3d667ea-96aa-48e6-8d36-4da4e6157069",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Pepare prompts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa15f752-9d1f-4f29-9b56-14d4e6224dcf",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.prompts.chat import (\n",
+    "    ChatPromptTemplate,\n",
+    "    SystemMessagePromptTemplate,\n",
+    "    HumanMessagePromptTemplate,\n",
+    ")\n",
+    "QUERY_KEY = 'query'\n",
+    "\n",
+    "core_terms_prompt_template = ChatPromptTemplate.from_messages([\n",
+    "    SystemMessagePromptTemplate(\n",
+    "        prompt=PromptTemplate(\n",
+    "            template=\"Always response with comma separated list of chemical or biological terms identified in prompt.\",\n",
+    "            input_variables=[]\n",
+    "        )\n",
+    "    ),\n",
+    "    HumanMessagePromptTemplate(\n",
+    "        prompt=PromptTemplate(\n",
+    "            template=f\"{{{QUERY_KEY}}}\",\n",
+    "            input_variables=[QUERY_KEY],\n",
+    "        )\n",
+    "    )\n",
+    "])\n",
+    "core_terms_prompt_template"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee248fb0-b606-46e4-8989-948a41ca0be7",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Create output parser"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08883978-6bf6-401d-a26f-29eca24e761d",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.output_parsers import CommaSeparatedListOutputParser\n",
+    "output_parser = CommaSeparatedListOutputParser()\n",
+    "output_parser"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4204b681-7b63-4f06-8076-e1cde25a7261",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Create chain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3fe60f84-e54c-4903-84e3-05e2a9864cb8",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain import LLMChain\n",
+    "CORE_TERMS_KEY = 'core_terms'\n",
+    "core_terms_chain = LLMChain(\n",
+    "    llm=llm,\n",
+    "    prompt=core_terms_prompt_template,\n",
+    "    output_parser=output_parser,\n",
+    "    output_key=CORE_TERMS_KEY,\n",
+    "    verbose=True\n",
+    ")\n",
+    "core_terms_chain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0179e2f-146a-4d28-97f3-abdfa79713b6",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "161651c8-d245-43a9-b8a7-18aa0e1a48d2",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "core_terms = core_terms_chain.run(query)\n",
+    "core_terms"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24f4dca0-4c03-41bf-a916-817443a1850b",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# Find core terms eids"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5248bd24-f3ef-4316-95a1-dccb8a99862d",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Create chain construct to query db"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae4445d6-22a6-4178-a5f7-0dc4cc1f2674",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "### Prepare graph results output parser\n",
+    "Unwraps query output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "99d1e485-fa7d-456e-a66a-34433fa3ec9e",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from llmlib.utils.output_parsers.graph_results import GraphResultsOutputParser"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66a53296-7b73-48bb-b02f-12e56d30000e",
+   "metadata": {},
+   "source": [
+    "### Prepare chain construct with output parser"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1b3acd93-57da-4d93-8204-aeed3862cb25",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from llmlib.utils.chains.graph_query import GraphQueryChain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "546e6ec9-4de6-4931-a1b2-690701c752bb",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Get graph reference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a4a43b53-a0df-4c5c-adf4-18a7e60d32e3",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from llmlib.database import Neo4j"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2bf3d50-25b5-42fd-88ae-4d6fe3aec03e",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "graph = Neo4j().graph()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "619cfbef-3d3a-4319-b515-bcddabd85655",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Create chain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0831a9c4-32d0-419a-af0e-ee740bf24011",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "query_output_key = 'output'\n",
+    "EID_MAPPING_KEY = 'eid_mapping'\n",
+    "find_eids_chain = GraphQueryChain(\n",
+    "    query=f\"\"\"\n",
+    "UNWIND ${CORE_TERMS_KEY} AS term\n",
+    "MATCH (s:Synonym {{lowercase_name: toLower(term)}})<-[:HAS_SYNONYM]-(n)\n",
+    "WHERE n.eid IS NOT NULL\n",
+    "WITH DISTINCT s.name as term, collect(n.eid) as eids\n",
+    "RETURN apoc.text.format(\n",
+    "    \"{{term: %s, eid: [%s]}}\",\n",
+    "    [term, apoc.text.join(eids, ',')]\n",
+    ") as {query_output_key}\n",
+    "\"\"\",\n",
+    "    input_keys=[CORE_TERMS_KEY],\n",
+    "    output_key=EID_MAPPING_KEY,\n",
+    "    graph=graph,\n",
+    "    output_parser=GraphResultsOutputParser(key=query_output_key),\n",
+    "    verbose=True\n",
+    ")\n",
+    "find_eids_chain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3917447a-cff1-48db-a935-bafbd16f866d",
+   "metadata": {},
+   "source": [
+    "### Run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "589b9106-c144-48ec-b139-69a512151f1c",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "eid_mapping = find_eids_chain.run(core_terms)\n",
+    "eid_mapping"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9a04cf2-19a3-4bf6-b8c3-5a996820c9ac",
+   "metadata": {},
+   "source": [
+    "## Combine chains test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3d3e753-dd60-4d11-9be5-4ae546e51994",
+   "metadata": {},
+   "source": [
+    "### Combine chains"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "762a5ba9-8e42-4867-ad76-3e82eabb6d50",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import SimpleSequentialChain\n",
+    "chain = SimpleSequentialChain(\n",
+    "    chains=[\n",
+    "        core_terms_chain,\n",
+    "        find_eids_chain\n",
+    "    ],\n",
+    "    verbose=True\n",
+    ")\n",
+    "chain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d72f2714-571a-4747-b9b4-3ec8db36a9be",
+   "metadata": {},
+   "source": [
+    "### Test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f78e535-790b-42c2-aba7-57490e49257f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output = chain.run(query)\n",
+    "output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6214fa0-f74d-40b6-9c7f-2519568db3fe",
+   "metadata": {},
+   "source": [
+    "# Respond to inquiry given database context"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da63ae13-c0b2-4ef6-8b5b-a3842b9e80c3",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Get graph reference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "30dfe983-ab71-42b0-a159-1ab1d6301fe8",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from llmlib.database import Neo4j"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a08b693-a770-40fc-a0c2-0c2d78551488",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "graph = Neo4j().graph()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5104d3a5-ff86-4dae-be7b-79d8f236a9ea",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "### Show schema preview"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb795e6b-1ad9-44f7-9e1a-dce85bd18ac9",
+   "metadata": {
+    "editable": true,
+    "raw_mimetype": "",
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "graph.schema"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64527b5b-82cb-415e-9bbb-63daedd2f0c6",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Limit schema\n",
+    "\n",
+    "Whole schema is too big to fit into request context - limit it to required parts."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ae59ca1-93d2-496b-aa54-554e4661a1ef",
+   "metadata": {},
+   "source": [
+    "### Set utils"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65d66ba9-b760-45fa-80aa-9d06f1d0dfc2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result_parser_factory = lambda key: lambda result: list(map(lambda row: row[key], result))\n",
+    "output_result_parser = result_parser_factory('output')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81d6c3cf-1c02-491f-af31-b6dc7bb1db80",
+   "metadata": {},
+   "source": [
+    "### Get preselected node properties"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "92112ba0-c2f3-4153-ab82-4e6e49c5dcec",
+   "metadata": {
+    "editable": true,
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "preselected_node_properties = [ \"displayName\", \"eid\" ]\n",
+    "preselected_node_labels = [\"Compound\", \"Reaction\", \"EnzReaction\", \"Regulation\"]\n",
+    "node_properties_query = \"\"\"\n",
+    "CALL apoc.meta.data()\n",
+    "YIELD label, other, elementType, type, property\n",
+    "WHERE NOT type = \"RELATIONSHIP\" AND elementType = \"node\" AND label in $preselected_node_labels AND property in $preselected_node_properties\n",
+    "WITH label AS nodeLabels, collect({property:property, type:type}) AS properties\n",
+    "RETURN {labels: nodeLabels, properties: properties} AS output\n",
+    "\"\"\"\n",
+    "node_properties = output_result_parser(\n",
+    "    graph.query(\n",
+    "        node_properties_query,\n",
+    "        dict(\n",
+    "            preselected_node_properties=preselected_node_properties,\n",
+    "            preselected_node_labels=preselected_node_labels\n",
+    "        )\n",
+    "    )\n",
+    ")\n",
+    "node_properties"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "576cdc57-aa35-44eb-80a9-7d8e190a94eb",
+   "metadata": {},
+   "source": [
+    "### Get preselected relationship properties"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f59f8403-3e8a-448d-a410-7d70bc835ee3",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "preselected_relationship_types = [\"CONSUMED_BY\", \"PRODUCES\", \"CATALYZES\", \"REGULATES\"]\n",
+    "rel_properties_query = \"\"\"\n",
+    "CALL apoc.meta.data()\n",
+    "YIELD label, other, elementType, type, property\n",
+    "WHERE NOT type = \"RELATIONSHIP\" AND elementType = \"relationship\" AND type in $preselected_relationship_types\n",
+    "WITH label AS nodeLabels, collect({property:property, type:type}) AS properties\n",
+    "RETURN {type: nodeLabels, properties: properties} AS output\n",
+    "\"\"\"\n",
+    "relationships_properties = output_result_parser(\n",
+    "    graph.query(\n",
+    "        rel_properties_query,\n",
+    "        dict(\n",
+    "            preselected_relationship_types=preselected_relationship_types,\n",
+    "        )\n",
+    "    )\n",
+    ")\n",
+    "relationships_properties"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7dcccf1f-42f5-40c8-85e0-371a552302a2",
+   "metadata": {},
+   "source": [
+    "### Get preselected relationships"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d0bba8c-c3d6-425e-94d2-e7afc5d059ad",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "rel_query = \"\"\"\n",
+    "CALL apoc.meta.data()\n",
+    "YIELD label, other, elementType, type, property\n",
+    "WHERE type = \"RELATIONSHIP\" AND elementType = \"node\" AND property in $preselected_relationship_types AND label in $preselected_node_labels\n",
+    "UNWIND other AS other_node\n",
+    "WITH label, property, toString(other_node) as other_node\n",
+    "WHERE other_node in $preselected_node_labels\n",
+    "RETURN {start: label, type: property, end: toString(other_node)} AS output\n",
+    "\"\"\"\n",
+    "relationships = output_result_parser(\n",
+    "    graph.query(\n",
+    "        rel_query,\n",
+    "        dict(\n",
+    "            preselected_relationship_types=preselected_relationship_types,\n",
+    "            preselected_node_labels=preselected_node_labels\n",
+    "        )\n",
+    "    )\n",
+    ")\n",
+    "relationships"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6971ce7-21bd-4ec4-b231-ed805a8b334e",
+   "metadata": {},
+   "source": [
+    "### Compose schema"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab33653c-75ba-4a31-a78f-7750d1194d24",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "limited_schema = f\"\"\"\n",
+    "Node properties are the following:\n",
+    "{node_properties}\n",
+    "Relationship properties are the following:\n",
+    "{relationships_properties}\n",
+    "The relationships are the following:\n",
+    "{relationships}\n",
+    "\"\"\"\n",
+    "limited_schema"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1bd1da66-81cb-46d8-8148-71a8986046a3",
+   "metadata": {},
+   "source": [
+    "### Set schema on the graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1a9644c-23ab-42bb-a0b8-6f2873929b22",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "graph.schema = limited_schema\n",
+    "graph.structured_schema = {\n",
+    "    \"node_props\": {el[\"labels\"]: el[\"properties\"] for el in node_properties},\n",
+    "    \"rel_props\": {el[\"type\"]: el[\"properties\"] for el in relationships_properties},\n",
+    "    \"relationships\": relationships,\n",
+    "}\n",
+    "graph.structured_schema"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d0eadfb-2220-4085-bcbe-f00892d62560",
+   "metadata": {},
+   "source": [
+    "## Prepare prompts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bcc8a296-950e-4d3a-bf13-5c2244c78137",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "from langchain import OpenAI, PromptTemplate\n",
+    "\n",
+    "SCHEMA_KEY = 'schema'\n",
+    "EXAMPLES_KEY = 'examples'\n",
+    "\n",
+    "cypher_generation_template = f\"\"\"\n",
+    "Task:Generate Cypher statements to query a graph database.\n",
+    "Instructions:\n",
+    "Use only the provided relationship types and properties in the schema.\n",
+    "Do not use any other relationship types or properties that are not provided.\n",
+    "If quering for relationship filter out following eids: [\"PROTON\"]\n",
+    "Schema:\n",
+    "{{{SCHEMA_KEY}}}\n",
+    "Examples:\n",
+    "{{{EXAMPLES_KEY}}}\n",
+    "Note: Do not include any explanations or apologies in your responses.\n",
+    "Do not respond to any questions that might ask anything else than for you to construct a Cypher statements.\n",
+    "Do not include any text except the generated Cypher statements.\n",
+    "Do not wrap statements in brackets.\n",
+    "Use example queries.\n",
+    "\n",
+    "The question is:\n",
+    "{{{QUERY_KEY}}}\n",
+    "\"\"\"\n",
+    "\n",
+    "cypher_prompt = PromptTemplate(\n",
+    "    input_variables=[SCHEMA_KEY, EXAMPLES_KEY, QUERY_KEY], template=cypher_generation_template\n",
+    ")\n",
+    "cypher_prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e6dfb939-aab1-46a6-838f-c443b193d6a6",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.chains import GraphCypherQAChain\n",
+    "from langchain.prompts.chat import (\n",
+    "    ChatPromptTemplate,\n",
+    "    SystemMessagePromptTemplate,\n",
+    "    HumanMessagePromptTemplate,\n",
+    "    AIMessagePromptTemplate,\n",
+    ")\n",
+    "\n",
+    "graph_qa_prompt_template = ChatPromptTemplate.from_messages([\n",
+    "    SystemMessagePromptTemplate(\n",
+    "        prompt=PromptTemplate(\n",
+    "            template=\"You are an expert in field of biology and chemistry with access to graph database.\",\n",
+    "            input_variables=[]\n",
+    "        )\n",
+    "    ),\n",
+    "    HumanMessagePromptTemplate(\n",
+    "        prompt=PromptTemplate(\n",
+    "            template=f\"\"\"\n",
+    "                Step 1: For given inquiry identify core terms.\n",
+    "                Step 2: Return mapping from core term to list of database 'eid' for related entities.\n",
+    "                Inqury: {{{QUERY_KEY}}}\n",
+    "            \"\"\",\n",
+    "            input_variables=[QUERY_KEY],\n",
+    "        )\n",
+    "    ),\n",
+    "    AIMessagePromptTemplate(\n",
+    "        prompt=PromptTemplate(\n",
+    "            template=f\"{{{EID_MAPPING_KEY}}}\",\n",
+    "            input_variables=[EID_MAPPING_KEY]\n",
+    "        )\n",
+    "    ),\n",
+    "    HumanMessagePromptTemplate(prompt=cypher_prompt)\n",
+    "])\n",
+    "graph_qa_prompt_template"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b720d7b3-31cd-4ec8-afac-f37e76a6e96f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qa_prompt_template = f\"\"\"You are an assistant that helps to form nice and human understandable answers.\n",
+    "The information part contains the provided information that you can use to construct an answer.\n",
+    "Make the answer sound as a response to the question. Do not mention that you based the result on the given information.\n",
+    "Information:\n",
+    "{{context}}\n",
+    "\n",
+    "Question:\n",
+    "{{{QUERY_KEY}}}\n",
+    "Helpful Answer:\"\"\"\n",
+    "qa_prompt = PromptTemplate(\n",
+    "    input_variables=[\"context\", QUERY_KEY], template=qa_prompt_template\n",
+    ")\n",
+    "qa_prompt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c8e15d2-07ca-48a4-8ea0-654387f0311d",
+   "metadata": {},
+   "source": [
+    "## Create chain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "457c788b-7939-4348-be6b-fcda1815c821",
+   "metadata": {},
+   "source": [
+    "#### Adapt GraphCypherQAChain to accept custom input keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e5eeaa8-8bcf-45bd-8ebf-d9dd50d54634",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llmlib.utils.chains.graph_cypher_qa_chain import GraphCypherQAChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b4b3b5c-ba8a-40cd-85f1-8de2cf50c959",
+   "metadata": {
+    "editable": true,
+    "raw_mimetype": "",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "RESPONSE_KEY = 'response'\n",
+    "graph_qa_chain = GraphCypherQAChain.from_llm(\n",
+    "    ChatOpenAI(temperature=0),\n",
+    "    graph=graph,\n",
+    "    verbose=True,\n",
+    "    cypher_prompt=graph_qa_prompt_template,\n",
+    "    qa_prompt=qa_prompt,\n",
+    "    output_key=RESPONSE_KEY,\n",
+    "    return_intermediate_steps=True\n",
+    ")\n",
+    "graph_qa_chain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b682554-65d3-454d-b029-1dde12cbb25a",
+   "metadata": {},
+   "source": [
+    "### Declare query examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "975185f8-ef84-4167-8493-de2ed929f263",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_relation_type_str = \"|\".join(preselected_relationship_types)\n",
+    "max_search_depth = 5\n",
+    "examples=[\n",
+    "    f\"\"\"\n",
+    "    // Get shortest path between nodes\n",
+    "    MATCH (a), (b)\n",
+    "    WHERE a.eid in <list_of_term_a_eids> AND b.eid in <list_of_term_b_eids>\n",
+    "    MATCH path=allShortestPaths((a)-[:{all_relation_type_str}*1..{max_search_depth}]-(b))\n",
+    "    RETURN [e in apoc.path.elements(path) | coalesce(e.eid, type(e))] as path LIMIT 1;\n",
+    "    \"\"\",\n",
+    "    \"\"\"\n",
+    "    // Get nodes information\n",
+    "    UNWIND <list_of_eids> as eid\n",
+    "    MATCH (node {{eid: eid}}\n",
+    "    RETURN node;\n",
+    "    \"\"\"\n",
+    "]\n",
+    "examples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee716f40-cb4a-4c24-8ccd-c428134b52a9",
+   "metadata": {},
+   "source": [
+    "### Declare run parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "068b52ac-bf35-44ab-a06a-e224a517018b",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "graph_qa_chain_inputs = {\n",
+    "    QUERY_KEY: query,\n",
+    "    EID_MAPPING_KEY: eid_mapping,\n",
+    "    EXAMPLES_KEY: examples\n",
+    "}\n",
+    "graph_qa_chain_inputs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4706a36a-ee22-457f-9802-4ac160a70db8",
+   "metadata": {},
+   "source": [
+    "### Run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01645dd2-b34c-4e75-a84b-03b44966619a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graph_qa_chain.run(graph_qa_chain_inputs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "764e609b-dec4-4189-8d8e-0b3b313b9f4c",
+   "metadata": {},
+   "source": [
+    "### Combine chains"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0255bbee-99d9-4345-aa9b-28bb55d23a3c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import SequentialChain\n",
+    "chain = SequentialChain(\n",
+    "    chains=[\n",
+    "        core_terms_chain,\n",
+    "        find_eids_chain,\n",
+    "        graph_qa_chain\n",
+    "    ],\n",
+    "    input_variables=[QUERY_KEY, EXAMPLES_KEY],\n",
+    "    output_variables=[RESPONSE_KEY],\n",
+    "    verbose=True\n",
+    ")\n",
+    "chain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32d92363-5fcb-4dfa-8b56-662335022013",
+   "metadata": {},
+   "source": [
+    "### Test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f3536688-2b27-4f13-8bc5-971f8582e1c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output = chain.run({\n",
+    "  QUERY_KEY: query,\n",
+    "  EXAMPLES_KEY: examples\n",
+    "})\n",
+    "output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "151c5471-219c-4a70-a58f-269ea8ef9515",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output = chain.run({\n",
+    "  QUERY_KEY: \"What is the relationship between Zn2+ and glcD?\",\n",
+    "  EXAMPLES_KEY: examples\n",
+    "})\n",
+    "output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d2441ff0-e32a-4fe0-9076-93d87a9a34bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output = chain.run({\n",
+    "  QUERY_KEY: \"What is the relationship between INHBA and MTMR4?\",\n",
+    "  EXAMPLES_KEY: examples\n",
+    "})\n",
+    "output"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/llm/develop/Using query retriever.ipynb b/llm/develop/Using query retriever.ipynb
new file mode 100644
index 0000000000..e845192dfb
--- /dev/null
+++ b/llm/develop/Using query retriever.ipynb	
@@ -0,0 +1,606 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6fda1acd-9514-4db5-ba8e-68255beb4c51",
+   "metadata": {},
+   "source": [
+    "# Init"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "feb21004",
+   "metadata": {
+    "collapsed": false,
+    "is_executing": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "98bb8a3a-1acb-4fab-a8dd-c7671b684fab",
+   "metadata": {
+    "is_executing": true
+   },
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'app'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mapp\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m app\n\u001b[1;32m      2\u001b[0m context \u001b[38;5;241m=\u001b[39m app\u001b[38;5;241m.\u001b[39mapp_context()\n\u001b[1;32m      3\u001b[0m context\u001b[38;5;241m.\u001b[39mpush()\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'app'"
+     ]
+    }
+   ],
+   "source": [
+    "from app import app\n",
+    "context = app.app_context()\n",
+    "context.push()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77a6dda5-ebc3-4d0a-8c22-62295460d871",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fbb6602c",
+   "metadata": {
+    "collapsed": false,
+    "is_executing": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from develop.utils import formatters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd918ef6-ec05-420f-8dc4-3a84e50de805",
+   "metadata": {},
+   "source": [
+    "## Inputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d0ad8eda-d372-4002-b609-42899582eb2a",
+   "metadata": {
+    "is_executing": true
+   },
+   "outputs": [],
+   "source": [
+    "query=\"What is the relationship between Zn2+ and glycolate?\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "877af485-bd94-4352-a511-d25d3e561b87",
+   "metadata": {},
+   "source": [
+    "## Initial check\n",
+    "\n",
+    "Current response:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f791641-1009-4fb6-b1ea-26521b8dddd2",
+   "metadata": {
+    "is_executing": true
+   },
+   "outputs": [],
+   "source": [
+    "import openai\n",
+    "response = openai.ChatCompletion.create(\n",
+    "    messages=[\n",
+    "        { \"role\": \"user\", \"content\": query }\n",
+    "    ],\n",
+    "    model=\"gpt-3.5-turbo\",\n",
+    "    temperature=0\n",
+    ")\n",
+    "response"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c0298d0-b147-4971-9606-d871307ebe56",
+   "metadata": {},
+   "source": [
+    "# Compose chain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ac517bb-5e7c-4562-9e49-25dbd2658122",
+   "metadata": {},
+   "source": [
+    "## Init llm model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7c93291-a342-485f-950e-97d12e528bfc",
+   "metadata": {
+    "editable": true,
+    "is_executing": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "llm = ChatOpenAI(temperature=0, verbose=True)\n",
+    "llm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3d667ea-96aa-48e6-8d36-4da4e6157069",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Pepare prompts for core term extraction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa15f752-9d1f-4f29-9b56-14d4e6224dcf",
+   "metadata": {
+    "editable": true,
+    "is_executing": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.prompts.chat import (\n",
+    "    ChatPromptTemplate,\n",
+    "    SystemMessagePromptTemplate,\n",
+    "    HumanMessagePromptTemplate,\n",
+    ")\n",
+    "QUERY_KEY = 'query'\n",
+    "\n",
+    "core_terms_prompt_template = ChatPromptTemplate.from_messages([\n",
+    "    SystemMessagePromptTemplate(\n",
+    "        prompt=PromptTemplate(\n",
+    "            template=\"Always response with comma separated list of chemical or biological terms identified in prompt.\",\n",
+    "            input_variables=[]\n",
+    "        )\n",
+    "    ),\n",
+    "    HumanMessagePromptTemplate(\n",
+    "        prompt=PromptTemplate(\n",
+    "            template=f\"{{{QUERY_KEY}}}\",\n",
+    "            input_variables=[QUERY_KEY],\n",
+    "        )\n",
+    "    )\n",
+    "])\n",
+    "core_terms_prompt_template"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27fbf490-99f0-431e-ac65-7c3870ed3ce3",
+   "metadata": {},
+   "source": [
+    "## Define embedding model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26df7344-3bd5-41c4-9522-0ef5270a20c6",
+   "metadata": {
+    "is_executing": true
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings import OpenAIEmbeddings\n",
+    "embeddings = OpenAIEmbeddings()\n",
+    "embeddings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a09da740-e364-487f-899a-ea83c6dd448b",
+   "metadata": {},
+   "source": [
+    "## Define vectorstore"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d9301443-d21d-463e-bf69-a0a83fb633bb",
+   "metadata": {
+    "is_executing": true
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.vectorstores import Chroma"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2a5b1d9-1f22-4afb-8886-2914f24abd4c",
+   "metadata": {
+    "is_executing": true
+   },
+   "outputs": [],
+   "source": [
+    "# Vectorstore\n",
+    "vectorstore = Chroma(embedding_function=embeddings, persist_directory=\"./chroma_db_oai\")\n",
+    "vectorstore"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "546e6ec9-4de6-4931-a1b2-690701c752bb",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Get graph reference"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9649640e-0d07-4978-8840-2229c3cee419",
+   "metadata": {},
+   "source": [
+    "### Define neo4j connector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a4a43b53-a0df-4c5c-adf4-18a7e60d32e3",
+   "metadata": {
+    "editable": true,
+    "is_executing": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from llmlib.database import Neo4j"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2bf3d50-25b5-42fd-88ae-4d6fe3aec03e",
+   "metadata": {
+    "editable": true,
+    "is_executing": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "graph = Neo4j().graph()\n",
+    "graph.schema = ''  # Overwrite schema to avoid it to be loaded from db (lengthy process not ussed in here)\n",
+    "graph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55ba2794-7262-4008-977d-141a26b00ef9",
+   "metadata": {},
+   "source": [
+    "#### Init graph search API wrapper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07435c6b-cf54-492d-95e0-692e23a88768",
+   "metadata": {
+    "is_executing": true
+   },
+   "outputs": [],
+   "source": [
+    "from llmlib.utils.search.cypher_search_api_wrapper import CypherSearchAPIWrapper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a80ffecb-7d05-4ac6-b4c8-607eeecfaa3d",
+   "metadata": {
+    "is_executing": true
+   },
+   "outputs": [],
+   "source": [
+    "search = CypherSearchAPIWrapper(\n",
+    "    graph=graph,\n",
+    "    verbose=True,\n",
+    "    return_intermediate_steps=True\n",
+    ")\n",
+    "search"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40d4403a-d125-4074-9cac-6ea152e3004f",
+   "metadata": {},
+   "source": [
+    "## Compose QA chain with graph retriever"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4653c28a-f701-4c7a-abae-0ac19f7ba79c",
+   "metadata": {},
+   "source": [
+    "### Init graph retriever"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12c53834-4d0f-488c-8609-6d6ab76f11e0",
+   "metadata": {
+    "is_executing": true
+   },
+   "outputs": [],
+   "source": [
+    "from llmlib.utils.retrievers.graph_search_retriever import GraphSearchRetriever"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0d27a79-56ec-4203-bae3-23a0767fc0c6",
+   "metadata": {
+    "is_executing": true
+   },
+   "outputs": [],
+   "source": [
+    "retriever = GraphSearchRetriever.from_llm(\n",
+    "    vectorstore=vectorstore,\n",
+    "    llm=llm,\n",
+    "    graph_search=search,\n",
+    "    prompt=core_terms_prompt_template,\n",
+    "    verbose=True,\n",
+    "    return_intermediate_steps=True\n",
+    ")\n",
+    "retriever"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea067678-3976-4e77-beef-3fbb67a51d37",
+   "metadata": {},
+   "source": [
+    "### Define callback handler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2fa0358c-5636-46cc-a596-46feb7a56fb0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.callbacks.stdout import StdOutCallbackHandler\n",
+    "handler = StdOutCallbackHandler()\n",
+    "handler"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb42705c-7ee1-4020-9808-c02fd7a909bf",
+   "metadata": {},
+   "source": [
+    "### Init QA chain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "301da357-d958-4915-92b7-643dc33e0d55",
+   "metadata": {
+    "is_executing": true
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.chains import RetrievalQA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d6c9335f-c2dd-458d-bbf8-6d1de5c59d10",
+   "metadata": {
+    "is_executing": true
+   },
+   "outputs": [],
+   "source": [
+    "qa = RetrievalQA.from_chain_type(\n",
+    "    llm=llm,\n",
+    "    chain_type=\"stuff\",\n",
+    "    retriever=retriever,\n",
+    "    verbose=True,\n",
+    "    callbacks=[handler]\n",
+    ")\n",
+    "qa"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ba40ea3-5937-460c-a6f1-8ee83b3357d4",
+   "metadata": {},
+   "source": [
+    "# Test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99bd213f-349c-4966-9259-c6714bcc9c94",
+   "metadata": {},
+   "source": [
+    "## Initial query"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "275c8dc5-980e-4adf-8973-561c10914c0b",
+   "metadata": {
+    "is_executing": true
+   },
+   "outputs": [],
+   "source": [
+    "qa.run(query, callbacks=[handler])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b2c4008-081b-44cf-b71b-4b26e75f43d5",
+   "metadata": {},
+   "source": [
+    "## Check background noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d2441ff0-e32a-4fe0-9076-93d87a9a34bc",
+   "metadata": {
+    "is_executing": true
+   },
+   "outputs": [],
+   "source": [
+    "qa.run(\"What is the relationship between INHBA and MTMR4?\", callbacks=[handler])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1909e2f9-de55-46b9-a13c-8e911366f954",
+   "metadata": {},
+   "source": [
+    "## Check different qa stuffing techniques"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9676eb1f-ca4f-4df3-a0f5-9490e3451196",
+   "metadata": {
+    "is_executing": true
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.chains import RetrievalQA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a52e9184-4868-4bfc-ba3f-f3d3d3c02246",
+   "metadata": {
+    "is_executing": true
+   },
+   "outputs": [],
+   "source": [
+    "qa = RetrievalQA.from_chain_type(\n",
+    "    llm=llm,\n",
+    "    chain_type=\"map_reduce\",\n",
+    "    retriever=retriever,\n",
+    "    verbose=True,\n",
+    "    callbacks=[handler]\n",
+    ")\n",
+    "qa"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47f68be0-571b-4425-abde-d945aa647612",
+   "metadata": {
+    "is_executing": true
+   },
+   "outputs": [],
+   "source": [
+    "qa.run(query, callbacks=[handler])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e81092f0-ca74-4c22-9d57-a296beeed009",
+   "metadata": {
+    "is_executing": true
+   },
+   "outputs": [],
+   "source": [
+    "qa.run(\"What is the relationship between INHBA and MTMR4?\", callbacks=[handler])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/llm/develop/__init__.py b/llm/develop/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/llm/develop/utils/__init__.py b/llm/develop/utils/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/llm/develop/utils/callback_manager.py b/llm/develop/utils/callback_manager.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/llm/develop/utils/formatters/__init__.py b/llm/develop/utils/formatters/__init__.py
new file mode 100644
index 0000000000..1b18c0bb2e
--- /dev/null
+++ b/llm/develop/utils/formatters/__init__.py
@@ -0,0 +1,12 @@
+from IPython import get_ipython
+from .html import html_formatters
+
+
+def register_formatters(type_: str, formatters: dict):
+    ipython_formatters = get_ipython().display_formatter.formatters
+    ipython_html_formatter = ipython_formatters[type_]
+    for cls, formatter in formatters.items():
+        ipython_html_formatter.for_type(cls, formatter)
+
+
+register_formatters('text/html', html_formatters)
diff --git a/llm/develop/utils/formatters/html.py b/llm/develop/utils/formatters/html.py
new file mode 100644
index 0000000000..9d321c982a
--- /dev/null
+++ b/llm/develop/utils/formatters/html.py
@@ -0,0 +1,98 @@
+import string
+
+from IPython import display
+from jinja2 import Environment, PackageLoader
+from langchain import PromptTemplate, LLMChain
+from langchain.chains import SimpleSequentialChain, SequentialChain
+
+from langchain.chat_models import ChatOpenAI
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.graphs import Neo4jGraph
+from langchain.prompts import ChatPromptTemplate
+from langchain.prompts.chat import BaseStringMessagePromptTemplate
+from llmlib.utils.search.cypher_search_api_wrapper import CypherSearchAPIWrapper
+from openai.openai_object import OpenAIObject
+
+from llmlib.utils.chains.graph_query import GraphQueryChain
+
+from llmlib.utils.chains.graph_cypher_qa_chain import GraphCypherQAChain
+from pydantic.v1 import BaseModel
+
+from .utils import format_object_factory, register_filter
+
+_html_env = Environment(
+    loader=PackageLoader('develop.utils.formatters', 'html_templates'),
+    lstrip_blocks=True,
+    trim_blocks=True,
+)
+
+# noinspection PyProtectedMember
+register_filter(
+    _html_env,
+    lambda *args, **kwargs: display.Code(*args, **kwargs)._repr_html_(),
+    'code',
+)
+register_filter(
+    _html_env,
+    lambda t, *args, **kwargs: string.Template(t).safe_substitute(*args, **kwargs),
+    'template',
+)
+
+
+def pydantic_model_formatter(model: BaseModel) -> str:
+    kwargs_exclude_defaults = model.dict(exclude_defaults=True)
+    return _html_env.get_template('pydantic_model.html.jinja2').render(
+        model=model,
+        not_default_keys=tuple(kwargs_exclude_defaults.keys()),
+    )
+
+
+html_formatters = {
+    ChatPromptTemplate: lambda pt: _html_env.get_template(
+        'chat_prompt_template.html.jinja2'
+    ).render(pt=pt),
+    BaseStringMessagePromptTemplate: lambda pt: _html_env.get_template(
+        'base_string_message_prompt_template.html.jinja2'
+    ).render(pt=pt),
+    PromptTemplate: lambda pt: _html_env.get_template(
+        'prompt_template.html.jinja2'
+    ).render(pt=pt),
+    LLMChain: lambda chain: _html_env.get_template('llm_chain.html.jinja2').render(
+        chain=chain
+    ),
+    SimpleSequentialChain: lambda chain: _html_env.get_template(
+        'sequential_chain.html.jinja2'
+    ).render(chain=chain),
+    SequentialChain: lambda chain: _html_env.get_template(
+        'sequential_chain.html.jinja2'
+    ).render(chain=chain),
+    list: lambda l: _html_env.get_template('list.html.jinja2').render(l=l),
+    dict: lambda obj: _html_env.get_template('dict.html.jinja2').render(obj=obj),
+    str: lambda s: _html_env.get_template('str.html.jinja2').render(s=s),
+    GraphQueryChain: lambda chain: _html_env.get_template(
+        'graph_query_chain.html.jinja2'
+    ).render(chain=chain),
+    GraphCypherQAChain: lambda chain: _html_env.get_template(
+        'graph_cypher_qa_chain.html.jinja2'
+    ).render(chain=chain),
+    Neo4jGraph: lambda graph: _html_env.get_template('neo4j_graph.html.jinja2').render(
+        graph=graph
+    ),
+    OpenAIObject: lambda obj: _html_env.get_template(
+        'openai_object.html.jinja2'
+    ).render(obj=obj),
+    ChatOpenAI: lambda obj: _html_env.get_template('openai_chat.html.jinja2').render(
+        obj=obj
+    ),
+    OpenAIEmbeddings: lambda obj: _html_env.get_template(
+        'openai_embeddings.html.jinja2'
+    ).render(obj=obj),
+    BaseModel: pydantic_model_formatter,
+    CypherSearchAPIWrapper: lambda obj: _html_env.get_template(
+        'cypher_search_api_wrapper.html.jinja2'
+    ).render(obj=obj),
+}
+
+register_filter(
+    _html_env, format_object_factory(html_formatters, _html_env.filters['escape'])
+)
diff --git a/llm/develop/utils/formatters/html_templates/base_string_message_prompt_template.html.jinja2 b/llm/develop/utils/formatters/html_templates/base_string_message_prompt_template.html.jinja2
new file mode 100644
index 0000000000..e1f06102c5
--- /dev/null
+++ b/llm/develop/utils/formatters/html_templates/base_string_message_prompt_template.html.jinja2
@@ -0,0 +1,6 @@
+{% from 'macros/collapsed_object_dict_dl.html.jinja2' import collapsed_object_dict_dl  %}
+{% call collapsed_object_dict_dl(pt, ('prompt',), 'prompt') %}
+    <dd>
+        {{ pt.prompt|format_object }}
+    </dd>
+{% endcall %}
diff --git a/llm/develop/utils/formatters/html_templates/chat_prompt_template.html.jinja2 b/llm/develop/utils/formatters/html_templates/chat_prompt_template.html.jinja2
new file mode 100644
index 0000000000..164abfed35
--- /dev/null
+++ b/llm/develop/utils/formatters/html_templates/chat_prompt_template.html.jinja2
@@ -0,0 +1,12 @@
+{% from 'macros/collapsed_object_dict_dl.html.jinja2' import collapsed_object_dict_dl %}
+{% call collapsed_object_dict_dl(pt, ('messages',), 'messages') %}
+    <dd>
+        <ul>
+            {% for message in pt.messages %}
+                <li>
+                    {{ message|format_object }}
+                </li>
+            {% endfor %}
+        </ul>
+    </dd>
+{% endcall %}
diff --git a/llm/develop/utils/formatters/html_templates/cypher_search_api_wrapper.html.jinja2 b/llm/develop/utils/formatters/html_templates/cypher_search_api_wrapper.html.jinja2
new file mode 100644
index 0000000000..74d70827cb
--- /dev/null
+++ b/llm/develop/utils/formatters/html_templates/cypher_search_api_wrapper.html.jinja2
@@ -0,0 +1,7 @@
+{% from 'macros/collapsed_object_dict_dl.html.jinja2' import collapsed_object_dict_dl %}
+{% call collapsed_object_dict_dl(obj) %}
+    <dt>related nodes query</dt>
+    <dd>{{ obj.related_nodes_query|template(obj.related_nodes_query_params)|code(language='cypher') }}</dd>
+    <dt>relationships query</dt>
+    <dd>{{ obj.relationships_query|template(obj.relationships_query_params)|code(language='cypher') }}</dd>
+{% endcall %}
diff --git a/llm/develop/utils/formatters/html_templates/dict.html.jinja2 b/llm/develop/utils/formatters/html_templates/dict.html.jinja2
new file mode 100644
index 0000000000..a5ec52d0b6
--- /dev/null
+++ b/llm/develop/utils/formatters/html_templates/dict.html.jinja2
@@ -0,0 +1,12 @@
+{% if obj %}
+<dl>
+    {% for key, value in obj|items %}
+        {% if key not in omit %}
+            <dt>{{ key|replace('_',' ') }}</dt>
+            <dd>{{ value|format_object }}&nbsp;</dd>
+        {% endif %}
+    {% endfor %}
+</dl>
+{% else %}
+{{ obj }}
+{% endif %}
diff --git a/llm/develop/utils/formatters/html_templates/graph_cypher_qa_chain.html.jinja2 b/llm/develop/utils/formatters/html_templates/graph_cypher_qa_chain.html.jinja2
new file mode 100644
index 0000000000..b9ab19651f
--- /dev/null
+++ b/llm/develop/utils/formatters/html_templates/graph_cypher_qa_chain.html.jinja2
@@ -0,0 +1,9 @@
+{% from 'macros/collapsed_object_dict_dl.html.jinja2' import collapsed_object_dict_dl %}
+{% call collapsed_object_dict_dl(pt, ('cypher_generation_chain', 'qa_chain', 'graph')) %}
+    <dt>graph</dt>
+    <dd>{{ chain.graph|format_object }}</dd>
+    <dt>cypher generation chain</dt>
+    <dd>{{ chain.cypher_generation_chain|format_object }}</dd>
+    <dt>qa chain</dt>
+    <dd>{{ chain.qa_chain|format_object }}</dd>
+{% endcall %}
diff --git a/llm/develop/utils/formatters/html_templates/graph_query_chain.html.jinja2 b/llm/develop/utils/formatters/html_templates/graph_query_chain.html.jinja2
new file mode 100644
index 0000000000..c19e4bd76d
--- /dev/null
+++ b/llm/develop/utils/formatters/html_templates/graph_query_chain.html.jinja2
@@ -0,0 +1,4 @@
+{% from 'macros/collapsed_object_dict_dl.html.jinja2' import collapsed_object_dict_dl %}
+{% call collapsed_object_dict_dl(pt, ('query'), 'query') %}
+    <dd>{{ chain.query|code }}</dd>
+{% endcall %}
diff --git a/llm/develop/utils/formatters/html_templates/list.html.jinja2 b/llm/develop/utils/formatters/html_templates/list.html.jinja2
new file mode 100644
index 0000000000..79518b6d67
--- /dev/null
+++ b/llm/develop/utils/formatters/html_templates/list.html.jinja2
@@ -0,0 +1,9 @@
+{% if l %}
+    <ul>
+        {% for value in l %}
+            <li>{{ value|format_object }}</li>
+        {% endfor %}
+    </ul>
+{% else %}
+    []
+{% endif %}
diff --git a/llm/develop/utils/formatters/html_templates/llm_chain.html.jinja2 b/llm/develop/utils/formatters/html_templates/llm_chain.html.jinja2
new file mode 100644
index 0000000000..5f20a265b1
--- /dev/null
+++ b/llm/develop/utils/formatters/html_templates/llm_chain.html.jinja2
@@ -0,0 +1,7 @@
+{% from 'macros/collapsed_object_dict_dl.html.jinja2' import collapsed_object_dict_dl %}
+{% call collapsed_object_dict_dl(chain, ('llm', 'prompt')) %}
+    <dt>llm</dt>
+    <dd>{{ chain.llm|format_object }}&nbsp;</dd>
+    <dt>prompt</dt>
+    <dd>{{ chain.prompt|format_object }}&nbsp;</dd>
+{% endcall %}
diff --git a/llm/develop/utils/formatters/html_templates/macros/collapsed_object_dict_dl.html.jinja2 b/llm/develop/utils/formatters/html_templates/macros/collapsed_object_dict_dl.html.jinja2
new file mode 100644
index 0000000000..521d24e179
--- /dev/null
+++ b/llm/develop/utils/formatters/html_templates/macros/collapsed_object_dict_dl.html.jinja2
@@ -0,0 +1,18 @@
+{% macro collapsed_object_dict_dl(obj, omit={}, dangling=None, details='') %}
+    <dl>
+        <details>
+            <summary>{{ obj.__class__.__name__ }}</summary>
+            {% for key, value in dict(obj)|items %}
+                {% if key not in omit %}
+                    <dt>{{ key|replace('_',' ')|escape }}</dt>
+                    <dd>{{ value|format_object }}</dd>
+                {% endif %}
+            {% endfor %}
+            {{ details }}
+            {% if dangling %}
+                <dt>{{ dangling }}</dt>
+            {% endif %}
+        </details>
+        {{ caller() }} {# content projection #}
+    </dl>
+{% endmacro %}
diff --git a/llm/develop/utils/formatters/html_templates/neo4j_graph.html.jinja2 b/llm/develop/utils/formatters/html_templates/neo4j_graph.html.jinja2
new file mode 100644
index 0000000000..1cb0875600
--- /dev/null
+++ b/llm/develop/utils/formatters/html_templates/neo4j_graph.html.jinja2
@@ -0,0 +1,7 @@
+<details>
+    <summary>Neo4jGraph</summary>
+    <dl>
+        <dt>graph</dt>
+        <dd>{{ graph.schema|format_object }}</dd>
+    </dl>
+</details>
diff --git a/llm/develop/utils/formatters/html_templates/openai_chat.html.jinja2 b/llm/develop/utils/formatters/html_templates/openai_chat.html.jinja2
new file mode 100644
index 0000000000..40a43acf32
--- /dev/null
+++ b/llm/develop/utils/formatters/html_templates/openai_chat.html.jinja2
@@ -0,0 +1,7 @@
+{% from 'macros/collapsed_object_dict_dl.html.jinja2' import collapsed_object_dict_dl %}
+{% call collapsed_object_dict_dl(obj, ('model_name', 'client', 'openai_api_key')) %}
+    <dt>client</dt>
+    <dd>{{ obj.client|escape }}&nbsp;</dd>
+    <dt>model name</dt>
+    <dd>{{ obj.model_name|escape }}&nbsp;</dd>
+{% endcall %}
diff --git a/llm/develop/utils/formatters/html_templates/openai_embeddings.html.jinja2 b/llm/develop/utils/formatters/html_templates/openai_embeddings.html.jinja2
new file mode 100644
index 0000000000..d0e4257171
--- /dev/null
+++ b/llm/develop/utils/formatters/html_templates/openai_embeddings.html.jinja2
@@ -0,0 +1,7 @@
+{% from 'macros/collapsed_object_dict_dl.html.jinja2' import collapsed_object_dict_dl %}
+{% call collapsed_object_dict_dl(obj, ('model', 'client', 'openai_api_key')) %}
+    <dt>client</dt>
+    <dd>{{ obj.client|escape }}&nbsp;</dd>
+    <dt>model name</dt>
+    <dd>{{ obj.model|escape }}&nbsp;</dd>
+{% endcall %}
diff --git a/llm/develop/utils/formatters/html_templates/openai_object.html.jinja2 b/llm/develop/utils/formatters/html_templates/openai_object.html.jinja2
new file mode 100644
index 0000000000..2c907e1632
--- /dev/null
+++ b/llm/develop/utils/formatters/html_templates/openai_object.html.jinja2
@@ -0,0 +1,26 @@
+{% from 'macros/collapsed_object_dict_dl.html.jinja2' import collapsed_object_dict_dl %}
+{% set usage %}
+<dt>usage</dt>
+<dd>
+    <dl>
+        <dt>prompt</dt>
+        <dd>{{ obj.usage.prompt_tokens|escape }} tokens</dd>
+        <dt>completion</dt>
+        <dd>{{ obj.usage.completion_tokens|escape }} tokens</dd>
+        <dt>total</dt>
+        <dd>{{ obj.usage.total_tokens|escape }} tokens</dd>
+    </dl>
+</dd>
+{% endset %}
+{% call collapsed_object_dict_dl(obj, ('usage', 'choices'), 'choices', usage) %}
+    <dd>
+        <dl>
+            {% for choice in obj.choices %}
+                <dt title="Index:&#9;{{ choice.index }}&#10;Finish reason:&#9;{{ choice.finish_reason }}">
+                    {{ choice.message.role }}
+                </dt>
+                <dd>{{ choice.message.content|escape }}</dd>
+            {% endfor %}
+        </dl>
+    </dd>
+{% endcall %}
diff --git a/llm/develop/utils/formatters/html_templates/prompt_template.html.jinja2 b/llm/develop/utils/formatters/html_templates/prompt_template.html.jinja2
new file mode 100644
index 0000000000..0b38a13772
--- /dev/null
+++ b/llm/develop/utils/formatters/html_templates/prompt_template.html.jinja2
@@ -0,0 +1,4 @@
+{% from 'macros/collapsed_object_dict_dl.html.jinja2' import collapsed_object_dict_dl %}
+{% call collapsed_object_dict_dl(pt, ('template',), 'template') %}
+    <dd style="white-space: pre-wrap">{{ pt.template|escape }}&nbsp;</dd>
+{% endcall %}
diff --git a/llm/develop/utils/formatters/html_templates/pydantic_model.html.jinja2 b/llm/develop/utils/formatters/html_templates/pydantic_model.html.jinja2
new file mode 100644
index 0000000000..7e0091f347
--- /dev/null
+++ b/llm/develop/utils/formatters/html_templates/pydantic_model.html.jinja2
@@ -0,0 +1,9 @@
+{% from 'macros/collapsed_object_dict_dl.html.jinja2' import collapsed_object_dict_dl %}
+{% call collapsed_object_dict_dl(model, not_default_keys) %}
+    {% for key, value in dict(model)|items %}
+        {% if key in not_default_keys %}
+            <dt>{{ key|replace('_',' ') }}</dt>
+            <dd>{{ value|format_object }}</dd>
+        {% endif %}
+    {% endfor %}
+{% endcall %}
diff --git a/llm/develop/utils/formatters/html_templates/sequential_chain.html.jinja2 b/llm/develop/utils/formatters/html_templates/sequential_chain.html.jinja2
new file mode 100644
index 0000000000..b44fe5c01c
--- /dev/null
+++ b/llm/develop/utils/formatters/html_templates/sequential_chain.html.jinja2
@@ -0,0 +1,12 @@
+{% from 'macros/collapsed_object_dict_dl.html.jinja2' import collapsed_object_dict_dl %}
+{% call collapsed_object_dict_dl(chain, ('chains',), 'chains') %}
+    <dd>
+        <ol>
+            {% for c in chain.chains %}
+                <li>
+                    {{ c|format_object }}
+                </li>
+            {% endfor %}
+        </ol>
+    </dd>
+{% endcall %}
diff --git a/llm/develop/utils/formatters/html_templates/str.html.jinja2 b/llm/develop/utils/formatters/html_templates/str.html.jinja2
new file mode 100644
index 0000000000..2cd58fa588
--- /dev/null
+++ b/llm/develop/utils/formatters/html_templates/str.html.jinja2
@@ -0,0 +1,7 @@
+<pre style="white-space: pre-wrap; display: inline;" title="string">
+    {%- if s -%}
+        {{ s|escape }}
+    {%- else -%}
+        ''
+    {%- endif -%}
+</pre>
diff --git a/llm/develop/utils/formatters/utils.py b/llm/develop/utils/formatters/utils.py
new file mode 100644
index 0000000000..f8ac291b9d
--- /dev/null
+++ b/llm/develop/utils/formatters/utils.py
@@ -0,0 +1,33 @@
+from typing import Dict, Callable, Any
+from jinja2 import Environment
+
+
+def format_object_factory(
+    _formatters: Dict[type, Callable[[Any], str]], escape: Callable[[str], str]
+):
+    def format_object(obj: Any) -> str:
+        """Format an object.
+
+        Args:
+            obj: object to format
+
+        Returns:
+            str
+        """
+        if type(obj) in _formatters:
+            return _formatters[type(obj)](obj)
+        for _formatter in _formatters:
+            if isinstance(obj, _formatter):
+                return _formatters[_formatter](obj)
+        if hasattr(obj, '_repr_html_'):
+            # noinspection PyProtectedMember
+            return obj._repr_html_()
+        return escape(repr(obj))
+
+    return format_object
+
+
+def register_filter(
+    env: Environment, filter_: Callable[[Any], Any], filter_name: str = None
+):
+    env.filters[filter_name if filter_name else filter_.__name__] = filter_
diff --git a/llm/llmlib/__init__.py b/llm/llmlib/__init__.py
new file mode 100644
index 0000000000..6b274abdd1
--- /dev/null
+++ b/llm/llmlib/__init__.py
@@ -0,0 +1 @@
+from .views import *
diff --git a/llm/llmlib/app.py b/llm/llmlib/app.py
new file mode 100644
index 0000000000..c2815db7ae
--- /dev/null
+++ b/llm/llmlib/app.py
@@ -0,0 +1,3 @@
+from .factory import create_app
+
+app = create_app()
diff --git a/llm/llmlib/config.py b/llm/llmlib/config.py
new file mode 100644
index 0000000000..c6569f3302
--- /dev/null
+++ b/llm/llmlib/config.py
@@ -0,0 +1,55 @@
+import os
+
+
+class Base:
+    """Default values"""
+
+    JWKS_URL = os.environ.get('JWKS_URL', None)
+    JWT_SECRET = os.environ.get('JWT_SECRET', 'secrets')
+    JWT_AUDIENCE = os.environ.get('JWT_AUDIENCE', None)
+    JWT_ALGORITHM = os.environ.get('JWT_ALGORITHM', 'HS256')
+
+    POSTGRES_HOST = os.environ.get('POSTGRES_HOST')
+    POSTGRES_PORT = os.environ.get('POSTGRES_PORT')
+    POSTGRES_USER = os.environ.get('POSTGRES_USER')
+    POSTGRES_PASSWORD = os.environ.get('POSTGRES_PASSWORD')
+    POSTGRES_DB = os.environ.get('POSTGRES_DB')
+
+    SQLALCHEMY_DATABASE_URI = 'postgresql://%s:%s@%s:%s/%s' % (
+        POSTGRES_USER,
+        POSTGRES_PASSWORD,
+        POSTGRES_HOST,
+        POSTGRES_PORT,
+        POSTGRES_DB,
+    )
+
+    NEO4J_HOST = os.environ.get('NEO4J_HOST', '0.0.0.0')
+    NEO4J_SCHEME = os.environ.get('NEO4J_SCHEME', 'bolt')
+    NEO4J_AUTH = os.environ.get('NEO4J_AUTH', 'neo4j/password')
+    NEO4J_PORT = os.environ.get('NEO4J_PORT', '7687')
+    NEO4J_DATABASE = os.environ.get('NEO4J_DATABASE')
+
+    ARANGO_HOST = os.environ.get('ARANGO_HOST', 'http://localhost:8529')
+    ARANGO_USERNAME = os.environ.get('ARANGO_USERNAME', '***ARANGO_USERNAME***')
+    ARANGO_PASSWORD = os.environ.get('ARANGO_PASSWORD', 'password')
+    ARANGO_DB_NAME = os.environ.get('ARANGO_DB_NAME', '***ARANGO_DB_NAME***')
+
+    OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY')
+
+    REDIS_HOST = os.environ.get('REDIS_HOST', 'localhost')
+    REDIS_PORT = port = os.environ.get('REDIS_PORT', '6379')
+    REDIS_PASSWORD = os.environ.get('REDIS_PASSWORD', '')
+    REDIS_SSL = os.environ.get('REDIS_SSL', 'false').lower() == 'true'
+    CACHE_REDIS_DB = os.environ.get('CACHE_REDIS_DB', '0')
+    CACHE_REDIS_URL = '{protocol}://:{password}@{host}:{port}/{db}'.format(
+        protocol='rediss' if REDIS_SSL else 'redis',
+        host=REDIS_HOST,
+        port=REDIS_PORT,
+        password=REDIS_PASSWORD,
+        db=CACHE_REDIS_DB,
+    )
+
+    # Optional for tracking the master branch for the build
+    GITHUB_HASH = os.environ.get('GITHUB_HASH', '__VERSION__')
+
+    FORWARD_STACKTRACE = False
diff --git a/llm/llmlib/database.py b/llm/llmlib/database.py
new file mode 100644
index 0000000000..edcff57604
--- /dev/null
+++ b/llm/llmlib/database.py
@@ -0,0 +1,75 @@
+from abc import ABC
+from functools import lru_cache
+
+from llmlib.interfaces import Graph
+from llmlib.utils.config import config
+
+from llmlib.utils.singleton import Singleton
+
+
+class Neo4j(Graph, metaclass=Singleton):
+    @lru_cache(maxsize=1)
+    def graph(self, name: str = None):
+        from llmlib.utils.lazy_neo4j_graph import LazyNeo4jGraph
+
+        host = config.get('NEO4J_HOST')
+        scheme = config.get('NEO4J_SCHEME')
+        port = config.get('NEO4J_PORT')
+        url = f'{scheme}://{host}:{port}'
+        username, password = config.get('NEO4J_AUTH', 'neo4j/password').split('/')
+        return LazyNeo4jGraph(
+            url=url,
+            username=username,
+            password=password,
+            database=name or config.get('NEO4J_DATABASE', 'neo4j'),
+        )
+
+
+class Arango(Graph, metaclass=Singleton):
+    _client = None
+
+    @property
+    def client(self):
+        if self._client is None:
+            from arango import ArangoClient
+
+            self._client = ArangoClient(hosts=config.get('ARANGO_HOST'))
+        return self._client
+
+    @lru_cache(maxsize=1)
+    def db(self, name: str = None):
+        return self.client.db(
+            name=name or config.get('ARANGO_DB_NAME', '_system'),
+            username=config.get('ARANGO_USERNAME', '***ARANGO_USERNAME***'),
+            password=config.get('ARANGO_PASSWORD', 'password'),
+        )
+
+    @lru_cache(maxsize=1)
+    def graph(self, name: str = None):
+        from langchain.graphs import ArangoGraph
+
+        return ArangoGraph(self.db(name))
+
+    def __del__(self):
+        if self._client is not None:
+            self._client.close()
+
+
+class RedisCache(ABC):
+    _client = None
+
+    @classmethod
+    def client(cls):
+        if cls._client is None:
+            from redis import Redis, BlockingConnectionPool
+
+            cls._client = Redis(
+                connection_pool=BlockingConnectionPool.from_url(
+                    config.get('CACHE_REDIS_URL')
+                )
+            )
+        return cls._client
+
+    def __del__(self):
+        if self._client is not None:
+            self._client.close()
diff --git a/llm/llmlib/exceptions/exceptions.py b/llm/llmlib/exceptions/exceptions.py
new file mode 100644
index 0000000000..3cd8a4d336
--- /dev/null
+++ b/llm/llmlib/exceptions/exceptions.py
@@ -0,0 +1,155 @@
+from abc import ABC
+from dataclasses import dataclass, field, asdict
+from http import HTTPStatus
+from typing import Tuple, Optional, Callable, Any
+
+import yaml
+from openai import OpenAIError
+
+from ..utils.transaction_id import transaction_id
+from ..utils.dataclass import CauseDefaultingDescriptor, LazyDefaulDescriptor
+from ..utils.string import compose_lines, indent_lines
+
+
+@dataclass(repr=False, eq=False, frozen=True)
+class BaseServerException(ABC):
+    title: str
+    message: Optional[str] = None
+    additional_msgs: Tuple[str, ...] = tuple()
+    fields: Optional[dict] = None
+    stacktrace: Optional[str] = None
+    version: Optional[str] = None
+
+    def __new__(cls, *args, **kwargs):
+        if cls == BaseServerException or BaseServerException in cls.__bases__:
+            raise TypeError("Cannot instantiate abstract class.")
+        return super().__new__(cls)
+
+    @property
+    def transaction_id(self):
+        return transaction_id
+
+    @property
+    def type(self):
+        return type(self).__name__
+
+    def to_dict(self):
+        return asdict(self)
+
+    def __repr__(self):
+        return f'<{self.type} {self.transaction_id}> {self.title}:{self.message}'
+
+    def __str__(self):
+        return compose_lines(
+            self.__repr__(),
+            *indent_lines(
+                # Additional msgs wrapped with new lines, or just singular newline
+                *(
+                    ('', *self.additional_msgs, '')
+                    if len(self.additional_msgs)
+                    else ('',)
+                ),
+                *yaml.dump(
+                    {
+                        k: v
+                        for k, v in self.to_dict().items()
+                        if v
+                        and k
+                        not in (
+                            # Already printed above
+                            'type',
+                            'transaction_id',
+                            'title',
+                            'message',
+                            'additional_msgs',
+                        )
+                    }
+                ).splitlines(),
+            ),
+        )
+
+
+@dataclass(repr=False, eq=False, frozen=True)
+class ServerException(Exception, BaseServerException):
+    """
+    Create a new exception.
+    :param title: the title of the error, which sometimes used on the client
+    :param message: a message that can be displayed to the user
+    :param additional_msgs: a list of messages that contain more details for the user
+    :param code: the error code
+    :param fields:
+    """
+
+    title: str = field(default=CauseDefaultingDescriptor("We're sorry!"))  # type: ignore
+    message: Optional[str] = field(  # type: ignore
+        default=CauseDefaultingDescriptor("Looks like something went wrong!")
+    )
+    additional_msgs: Tuple[str, ...] = field(
+        default=CauseDefaultingDescriptor(tuple())  # type: ignore
+    )
+    fields: Optional[dict] = field(default=CauseDefaultingDescriptor(None))  # type: ignore
+    code: HTTPStatus = field(  # type: ignore
+        default=CauseDefaultingDescriptor(HTTPStatus.INTERNAL_SERVER_ERROR)
+    )
+
+    @property
+    def type(self):
+        return type(self).__name__
+
+    @property
+    def transaction_id(self):
+        return transaction_id
+
+    def __repr__(self):
+        return f'<{self.type} {self.transaction_id}>\t{self.title}:\t{self.message}'
+
+    def __str__(self):
+        lines = [self.__repr__()]
+        try:
+            lines += [f'\t{key}:\t{value}' for key, value in self.fields.items()]
+        except Exception:
+            pass
+        return '\n'.join(lines)
+
+    def to_dict(self):
+        return asdict(self)
+
+
+@dataclass(repr=False, eq=False, frozen=True)
+class JWTTokenException(ServerException):
+    """Signals JWT token issue"""
+
+    code: HTTPStatus = HTTPStatus.UNAUTHORIZED
+
+
+@dataclass(repr=False, eq=False, frozen=True)
+class JWTAuthTokenException(JWTTokenException):
+    """Signals the JWT auth token has an issue"""
+
+    pass
+
+
+def openai_cause_accessor(prop: str, default) -> Callable[[Any, str], Any]:
+    def _accessor(obj, _):
+        if isinstance(obj.__cause__, OpenAIError):
+            value = getattr(obj.__cause__, prop)
+            if value:
+                return value
+        return default
+
+    return _accessor
+
+
+@dataclass(repr=False, eq=False, frozen=True)
+class OpenAiServerException(ServerException):
+    title: str = 'OpenAI Server Error'
+    message: str = field(  # type: ignore
+        default=LazyDefaulDescriptor(
+            openai_cause_accessor('user_message', "OpenAI Server Error")
+        )
+    )
+    code: HTTPStatus = field(  # type: ignore
+        default=LazyDefaulDescriptor(
+            openai_cause_accessor('http_status', HTTPStatus.INTERNAL_SERVER_ERROR)
+        )
+    )
diff --git a/llm/llmlib/exceptions/wrap_exceptions.py b/llm/llmlib/exceptions/wrap_exceptions.py
new file mode 100644
index 0000000000..5f5233bd9a
--- /dev/null
+++ b/llm/llmlib/exceptions/wrap_exceptions.py
@@ -0,0 +1,38 @@
+import functools
+from typing import Tuple, Union, Type, Protocol
+from .exceptions import ServerException
+
+
+class WrappingExceptionFactory(Protocol):
+    def __call__(self, exception: Exception, **kwargs) -> Exception:
+        ...
+
+
+def wrap_exceptions(
+    wrapping_exception: Union[Type[Exception], WrappingExceptionFactory],
+    wrapped_exceptions: Union[
+        Type[Exception], Tuple[Type[Exception], ...]
+    ] = ServerException,
+    **exception_kwargs
+):
+    """Decorator that reraises wrapping exception in case given exception occurs"""
+
+    def decorator(func):
+        @functools.wraps(func)
+        def wrapper(*args, **kwargs):
+            try:
+                return func(*args, **kwargs)
+            except wrapped_exceptions as e:
+                if isinstance(wrapping_exception, type):
+                    # noinspection PyArgumentList
+                    raise wrapping_exception(**exception_kwargs) from e
+                else:
+                    raise wrapping_exception(e, **exception_kwargs) from e
+
+        wrapper.__wrapped__ = func
+        return wrapper
+
+    return decorator
+
+
+__all__ = ["wrap_exceptions"]
diff --git a/llm/llmlib/factory.py b/llm/llmlib/factory.py
new file mode 100644
index 0000000000..a7224835e1
--- /dev/null
+++ b/llm/llmlib/factory.py
@@ -0,0 +1,22 @@
+from flask import Flask, jsonify
+import os
+
+from openai import OpenAIError
+
+from .schemas.error import OpenAiErrorResponseSchema
+from .services.chat_gpt import ChatGPT
+from .config import Base
+
+
+def handle_openai_error(ex):
+    return jsonify(OpenAiErrorResponseSchema().dump(ex)), ex.code
+
+
+def create_app():
+    app_name = os.environ.get('FLASK_APP', __name__)
+    app = Flask(app_name)
+    app.config.from_object(Base)
+    app.register_error_handler(OpenAIError, handle_openai_error)
+    ChatGPT.init_app(app)
+
+    return app
diff --git a/llm/llmlib/interfaces.py b/llm/llmlib/interfaces.py
new file mode 100644
index 0000000000..868bb9e6d2
--- /dev/null
+++ b/llm/llmlib/interfaces.py
@@ -0,0 +1,13 @@
+from abc import ABC, abstractmethod, ABCMeta
+from typing import TypedDict
+
+
+class Graph(ABC):
+    @abstractmethod
+    def graph(self, name: str):
+        ...
+
+
+class GraphRef(TypedDict):
+    database_type: str
+    database_name: str
diff --git a/llm/llmlib/schemas/__init__.py b/llm/llmlib/schemas/__init__.py
new file mode 100644
index 0000000000..f3f28d32ab
--- /dev/null
+++ b/llm/llmlib/schemas/__init__.py
@@ -0,0 +1,42 @@
+from marshmallow import Schema
+from marshmallow.fields import Str, Nested
+from marshmallow.validate import OneOf
+
+from .error import OpenAiErrorResponseSchema
+from .openai.chat_completions import ChatCompletionsRequestSchema
+from .openai.completions import CompletionsRequestSchema
+
+
+class ServerRequestSchema(Schema):
+    user = Str(required=True)
+    transaction_id = Str(required=True)
+
+
+class DBRequestSchema(Schema):
+    database_type = Str(validate=OneOf(['arango', 'neo4j']), default='arango')
+    database_name = Str()
+
+
+class GraphRequestSchema(ServerRequestSchema):
+    graph = Nested(DBRequestSchema())
+
+
+class GraphCompletionsRequestSchema(CompletionsRequestSchema, GraphRequestSchema):
+    pass
+
+
+class GraphChatCompletionsRequestSchema(
+    ChatCompletionsRequestSchema, GraphRequestSchema
+):
+    pass
+
+
+class GraphQARequestSchema(GraphRequestSchema):
+    query = Str(required=True)
+
+
+__all__ = [
+    'OpenAiErrorResponseSchema',
+    'GraphCompletionsRequestSchema',
+    'GraphChatCompletionsRequestSchema',
+]
diff --git a/llm/llmlib/schemas/error.py b/llm/llmlib/schemas/error.py
new file mode 100644
index 0000000000..c6f003d430
--- /dev/null
+++ b/llm/llmlib/schemas/error.py
@@ -0,0 +1,30 @@
+import traceback
+
+from flask import current_app
+from marshmallow import fields, Schema
+
+from llmlib.utils import transaction_id
+
+
+class OpenAiErrorResponseSchema(Schema):
+    """All errors are emitted with this schema."""
+
+    title = "OpenAI Server Error"
+    type = fields.Function(lambda obj: type(obj).__name__)
+    message = fields.Function(lambda obj: obj.user_message)
+    code = fields.Function(lambda obj: obj.http_status)
+    transaction_id = fields.Function(lambda obj: transaction_id)
+
+    stacktrace = fields.Method('get_stacktrace')
+
+    # noinspection PyMethodMayBeStatic
+    def get_stacktrace(self, ex):
+        if current_app.config.get('FORWARD_STACKTRACE'):
+            return ''.join(
+                traceback.format_exception(
+                    etype=type(ex), value=ex, tb=ex.__traceback__
+                )
+            )
+
+
+__all__ = ['OpenAiErrorResponseSchema']
diff --git a/llm/llmlib/schemas/fields.py b/llm/llmlib/schemas/fields.py
new file mode 100644
index 0000000000..e734d35e09
--- /dev/null
+++ b/llm/llmlib/schemas/fields.py
@@ -0,0 +1,17 @@
+from typing import Iterable
+
+from marshmallow import fields, ValidationError
+
+
+class UnionType(fields.Field):
+    def __init__(self, *args, types: Iterable[fields.Field], **kwargs):
+        super().__init__(*args, **kwargs)
+        self.types = types
+
+    def _deserialize(self, value, attr, data, **kwargs):
+        for field_type in self.types:
+            try:
+                return field_type.deserialize(value)
+            except ValidationError:
+                pass
+        raise ValidationError('Value does not match any of the types!')
diff --git a/llm/llmlib/schemas/openai/chat_completions.py b/llm/llmlib/schemas/openai/chat_completions.py
new file mode 100644
index 0000000000..31365a021e
--- /dev/null
+++ b/llm/llmlib/schemas/openai/chat_completions.py
@@ -0,0 +1,35 @@
+from marshmallow import fields, Schema, validate
+
+from ..fields import UnionType
+from .completions_base import CompletionsBaseRequestSchema
+
+
+class MessageSchema(Schema):
+    role = fields.String(
+        validate=fields.validate.OneOf(['system', 'user', 'assistant', 'function']),
+        required=True,
+    )
+    content = fields.String(nullable=True, required=True)
+    name = fields.String()
+    function_call = fields.Dict()
+
+
+class FunctionSchema(Schema):
+    name = fields.String(required=True)
+    description = fields.String()
+    parameters = fields.Dict(keys=fields.String(), values=fields.Raw(), required=True)
+
+
+class ChatCompletionsRequestSchema(CompletionsBaseRequestSchema):
+    model = fields.String(required=True)
+    messages = fields.List(fields.Nested(MessageSchema()), required=True)
+    functions = fields.List(fields.Nested(FunctionSchema()))
+    function_call = UnionType(
+        types=(
+            fields.String(validate=validate.OneOf(('none', 'all'))),
+            fields.Dict(
+                keys=fields.String(),
+                values=fields.String(),
+            ),
+        ),
+    )
diff --git a/llm/llmlib/schemas/openai/completions.py b/llm/llmlib/schemas/openai/completions.py
new file mode 100644
index 0000000000..f2a5c66d8e
--- /dev/null
+++ b/llm/llmlib/schemas/openai/completions.py
@@ -0,0 +1,11 @@
+from marshmallow import fields
+
+from .completions_base import CompletionsBaseRequestSchema
+
+
+class CompletionsRequestSchema(CompletionsBaseRequestSchema):
+    model = fields.String(required=True)
+    prompt = fields.String(required=True)
+    suffix = fields.String()
+    echo = fields.Boolean()
+    best_of = fields.Integer()
diff --git a/llm/llmlib/schemas/openai/completions_base.py b/llm/llmlib/schemas/openai/completions_base.py
new file mode 100644
index 0000000000..2760449eef
--- /dev/null
+++ b/llm/llmlib/schemas/openai/completions_base.py
@@ -0,0 +1,21 @@
+from marshmallow import fields, validate, Schema
+
+
+class CompletionsBaseRequestSchema(Schema):
+    timeout = fields.Float()
+    max_tokens = fields.Integer()
+    temperature = fields.Float(validate=validate.Range(min=0, max=2))
+    top_p = fields.Float()
+    n = fields.Integer()
+    stream = fields.Boolean()
+    logprobs = fields.Integer(validate=validate.Range(min=0, max=5), allow_none=True)
+    stop = fields.List(
+        fields.String(required=True), validate=validate.Length(min=0, max=4)
+    )
+    presence_penalty = fields.Float(validate=validate.Range(min=-2, max=2))
+    frequency_penalty = fields.Float(validate=validate.Range(min=-2, max=2))
+    logit_bias = fields.Dict(
+        keys=fields.String(),
+        values=fields.Float(validate=validate.Range(min=-100, max=100)),
+    )
+    user = fields.String()
diff --git a/llm/llmlib/services/chat_gpt.py b/llm/llmlib/services/chat_gpt.py
new file mode 100644
index 0000000000..798f0f55c3
--- /dev/null
+++ b/llm/llmlib/services/chat_gpt.py
@@ -0,0 +1,104 @@
+import openai
+from cachetools import Cache, cached
+from openai import OpenAIError
+
+from ..exceptions.wrap_exceptions import wrap_exceptions
+from ..exceptions.exceptions import OpenAiServerException, ServerException
+from .redis_cache import RedisCache
+
+
+class ChatGPT:
+    """Wrapper for OpenAI's Chat API"""
+
+    DELIMITER = "```"  # Used to escape user defined input
+
+    @staticmethod
+    def escape(text: str):
+        return text.replace(ChatGPT.DELIMITER, f'[ignored]')
+
+    class Completion(openai.Completion):
+        cache: Cache = RedisCache(
+            'ChatGPT', 'Completion', ex=3600 * 24 * 7  # Cache for a week
+        )
+        # Information which seems to be only avaliable in here:
+        # https://platform.openai.com/docs/models/model-endpoint-compatibility
+        _compatible_models = [
+            "text-davinci-003",
+            "text-davinci-002",
+            "text-davinci-001",
+            "text-curie-001",
+            "text-babbage-001",
+            "text-ada-001",
+            "davinci",
+            "curie",
+            "babbage",
+            "ada",
+        ]
+
+        @staticmethod
+        @cached(cache=cache)
+        def model_list(*args, **kwargs):
+            return [
+                model
+                for model in ChatGPT.Model.list(*args, **kwargs)
+                if model.get('id') in ChatGPT.Completion._compatible_models
+            ]
+
+        @staticmethod
+        @cached(cache=cache)
+        def create(*args, **kwargs):
+            return openai.Completion.create(*args, **kwargs)
+
+        @staticmethod
+        def create_stream(*args, **kwargs):
+            return openai.Completion.create(*args, **kwargs)
+
+    class ChatCompletion(openai.ChatCompletion):
+        cache: Cache = RedisCache(
+            'ChatGPT', 'ChatCompletion', ex=3600 * 24 * 7  # Cache for a week
+        )
+        # Information which seems to be only avaliable in here:
+        # https://platform.openai.com/docs/models/model-endpoint-compatibility
+        _compatible_models = [
+            "gpt-4",
+            "gpt-4-0613",
+            "gpt-4-32k",
+            "gpt-4-32k-0613",
+            "gpt-3.5-turbo",
+            "gpt-3.5-turbo-0613",
+            "gpt-3.5-turbo-16k",
+            "gpt-3.5-turbo-16k-0613",
+        ]
+
+        @staticmethod
+        @cached(cache=cache)
+        def model_list(*args, **kwargs):
+            return [
+                model
+                for model in ChatGPT.Model.list(*args, **kwargs)
+                if model.get('id') in ChatGPT.ChatCompletion._compatible_models
+            ]
+
+        @staticmethod
+        @cached(cache=cache)
+        def create(*args, **kwargs):
+            return openai.ChatCompletion.create(*args, **kwargs)
+
+        @staticmethod
+        def create_stream(*args, **kwargs):
+            return openai.ChatCompletion.create(*args, **kwargs)
+
+    class Model(openai.Model):
+        cache: Cache = RedisCache('ChatGPT', 'Model', ex=3600 * 24)  # Cache for a day
+
+        @staticmethod
+        @cached(cache=cache)
+        def list(*args, **kwargs):
+            return openai.Model.list(*args, **kwargs)['data']
+
+    @staticmethod
+    def init_app(app):
+        openai.api_key = app.config.get("OPENAI_API_KEY")
+
+
+__all__ = ["ChatGPT"]
diff --git a/llm/llmlib/services/redis_cache.py b/llm/llmlib/services/redis_cache.py
new file mode 100644
index 0000000000..a1bea1a7c2
--- /dev/null
+++ b/llm/llmlib/services/redis_cache.py
@@ -0,0 +1,86 @@
+""" Redis Cache """
+import json
+from typing import TypeVar, Generic, Callable, Tuple, Optional
+
+import redis
+from cachetools import Cache
+
+from ..utils.config import config
+
+DEFAULT_CACHE_SETTINGS = {'ex': 3600 * 24}
+
+_redis_server: Optional[redis.Redis] = None
+
+
+def get_redis_cache_server():
+    global _redis_server
+    if _redis_server is None:
+        _redis_server = redis.Redis(
+            connection_pool=redis.BlockingConnectionPool.from_url(
+                config.get('CACHE_REDIS_URL')
+            )
+        )
+    return _redis_server
+
+
+Key = TypeVar('Key')
+Value = TypeVar('Value')
+
+
+class RedisCache(Generic[Key, Value], Cache):
+    _prefixes: Tuple[str, ...]
+    _prefix_separator: str = ':'
+    _dumps: Callable[[Value], str]
+    _loads: Callable[[str], Value]
+    _cache_setting = DEFAULT_CACHE_SETTINGS
+
+    @property
+    def _redis(self) -> redis.Redis:
+        return get_redis_cache_server()
+
+    def __init__(
+        self,
+        *prefixes: str,
+        dumps: Callable[[Value], str] = json.dumps,
+        loads: Callable[[str], Value] = json.loads,
+        maxsize=float('inf'),
+        getsizeof=None,
+        **cache_setting,
+    ):
+        super().__init__(maxsize, getsizeof)
+        self._prefixes = prefixes
+        self._dumps = dumps
+        self._loads = loads
+        self._cache_setting = {**DEFAULT_CACHE_SETTINGS, **cache_setting}
+
+    def compose_key(self, key: Key) -> str:
+        return self._prefix_separator.join(
+            part for part in (*self._prefixes, str(key)) if part
+        )
+
+    def __getitem__(self, key: Key):
+        item = self._redis.get(self.compose_key(key))
+        if item is None:
+            return self.__missing__(key)
+        return self._loads(item)
+
+    def __setitem__(self, key, value):
+        self._redis.set(
+            self.compose_key(key), self._dumps(value), **self._cache_setting
+        )
+
+    def __delitem__(self, key):
+        self._redis.delete(self.compose_key(key))
+
+    def __contains__(self, key):
+        return self._redis.exists(self.compose_key(key))
+
+    def __iter__(self):
+        return self._redis.scan_iter()
+
+    def __len__(self):
+        return self._redis.dbsize()
+
+    @property
+    def currsize(self):
+        return len(self)
diff --git a/llm/llmlib/utils/__init__.py b/llm/llmlib/utils/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/llm/llmlib/utils/callbacks/base.py b/llm/llmlib/utils/callbacks/base.py
new file mode 100644
index 0000000000..bba0c75caf
--- /dev/null
+++ b/llm/llmlib/utils/callbacks/base.py
@@ -0,0 +1,55 @@
+from abc import abstractmethod
+from typing import Any, Type, List, Protocol, runtime_checkable
+
+from langchain.callbacks.base import BaseCallbackHandler
+from langchain.callbacks.manager import CallbackManager
+
+
+@runtime_checkable
+class GraphRetrieverHandlerProtocol(Protocol):
+    @abstractmethod
+    def on_related_nodes(self, nodes) -> Any:
+        """Callback for related nodes load."""
+
+    @abstractmethod
+    def on_related_relationships(self, relationships) -> Any:
+        """Callback for related relationships load."""
+
+
+class GraphRetrieverManagerMixin:
+    nodes: Any
+    relationships: Any
+
+    def on_related_nodes(self, nodes) -> Any:
+        """Callback for related nodes load."""
+        self.nodes = nodes
+
+    def on_related_relationships(self, relationships) -> Any:
+        """Callback for related relationships load."""
+        self.relationships = relationships
+
+
+class GraphCallbackManager:
+    handlers: List[BaseCallbackHandler]
+
+    @classmethod
+    def from_callback_manager(cls, callback_manager: Type[CallbackManager], **kwargs) -> Any:
+        return type(
+            'GraphCallbackManager',
+            (callback_manager, GraphCallbackManager),
+            kwargs
+        )
+
+    def on_related_nodes(self, nodes) -> Any:
+        """Callback for related nodes load."""
+
+        for handler in self.handlers:
+            if isinstance(handler, GraphRetrieverHandlerProtocol):
+                handler.on_related_nodes(nodes)
+
+    def on_related_relationships(self, relationships) -> Any:
+        """Callback for related relationships load."""
+
+        for handler in self.handlers:
+            if isinstance(handler, GraphRetrieverHandlerProtocol):
+                handler.on_related_relationships(relationships)
diff --git a/llm/llmlib/utils/callbacks/manager.py b/llm/llmlib/utils/callbacks/manager.py
new file mode 100644
index 0000000000..1d90cab1d7
--- /dev/null
+++ b/llm/llmlib/utils/callbacks/manager.py
@@ -0,0 +1,22 @@
+from typing import Any
+
+from langchain.callbacks.manager import ParentRunManager
+from .base import GraphRetrieverManagerMixin, GraphRetrieverHandlerProtocol
+
+
+class CallbackManagerForGraphRetrieverRun(ParentRunManager, GraphRetrieverManagerMixin):
+    """Callback manager for graph retriever run."""
+
+    def on_related_nodes(self, nodes) -> Any:
+        """Callback for related nodes load."""
+
+        for handler in self.handlers:
+            if isinstance(handler, GraphRetrieverHandlerProtocol):
+                handler.on_related_nodes(nodes)
+
+    def on_related_relationships(self, relationships) -> Any:
+        """Callback for related relationships load."""
+
+        for handler in self.handlers:
+            if isinstance(handler, GraphRetrieverHandlerProtocol):
+                handler.on_related_relationships(relationships)
diff --git a/llm/llmlib/utils/chains/graph_cypher_qa_chain.py b/llm/llmlib/utils/chains/graph_cypher_qa_chain.py
new file mode 100644
index 0000000000..9984cad06a
--- /dev/null
+++ b/llm/llmlib/utils/chains/graph_cypher_qa_chain.py
@@ -0,0 +1,211 @@
+"""Question answering over a graph."""
+from __future__ import annotations
+
+import re
+from typing import Any, Dict, List, Optional
+
+from langchain.callbacks.manager import CallbackManagerForChainRun
+from langchain.chains.base import Chain
+from langchain.chains.graph_qa.prompts import CYPHER_GENERATION_PROMPT, CYPHER_QA_PROMPT
+from langchain.chains.llm import LLMChain
+from langchain.graphs.neo4j_graph import Neo4jGraph
+from langchain.pydantic_v1 import Field
+from langchain.schema import BasePromptTemplate
+from langchain.schema.language_model import BaseLanguageModel
+
+INTERMEDIATE_STEPS_KEY = "intermediate_steps"
+
+
+def extract_cypher(text: str) -> str:
+    """Extract Cypher code from a text.
+
+    Args:
+        text: Text to extract Cypher code from.
+
+    Returns:
+        Cypher code extracted from the text.
+    """
+    # The pattern to find Cypher code enclosed in triple backticks
+    pattern = r"```(.*?)```"
+
+    # Find all matches in the input text
+    matches = re.findall(pattern, text, re.DOTALL)
+
+    return matches[0] if matches else text
+
+
+def construct_schema(
+    structured_schema: Dict[str, Any],
+    include_types: List[str],
+    exclude_types: List[str],
+) -> str:
+    """Filter the schema based on included or excluded types"""
+
+    def filter_func(x: str) -> bool:
+        return x in include_types if include_types else x not in exclude_types
+
+    filtered_schema = {
+        "node_props": {
+            k: v
+            for k, v in structured_schema.get("node_props", {}).items()
+            if filter_func(k)
+        },
+        "rel_props": {
+            k: v
+            for k, v in structured_schema.get("rel_props", {}).items()
+            if filter_func(k)
+        },
+        "relationships": [
+            r
+            for r in structured_schema.get("relationships", [])
+            if all(filter_func(r[t]) for t in ["start", "end", "type"])
+        ],
+    }
+
+    return (
+        f"Node properties are the following: \n {filtered_schema['node_props']}\n"
+        f"Relationships properties are the following: \n {filtered_schema['rel_props']}"
+        "\nRelationships are: \n"
+        + str(
+            [
+                f"(:{el['start']})-[:{el['type']}]->(:{el['end']})"
+                for el in filtered_schema["relationships"]
+            ]
+        )
+    )
+
+
+class GraphCypherQAChain(Chain):
+    """Chain for question-answering against a graph by generating Cypher statements."""
+
+    graph: Neo4jGraph = Field(exclude=True)
+    cypher_generation_chain: LLMChain
+    qa_chain: LLMChain
+    graph_schema: str
+    input_key: str = "query"  #: :meta private:
+    output_key: str = "result"  #: :meta private:
+    top_k: int = 10
+    """Number of results to return from the query"""
+    return_intermediate_steps: bool = False
+    """Whether or not to return the intermediate steps along with the final answer."""
+    return_direct: bool = False
+    """Whether or not to return the result of querying the graph directly."""
+
+    @property
+    def input_keys(self) -> List[str]:
+        """Return the input keys.
+
+        :meta private:
+        """
+        return [self.input_key]
+
+    @property
+    def output_keys(self) -> List[str]:
+        """Return the output keys.
+
+        :meta private:
+        """
+        _output_keys = [self.output_key]
+        return _output_keys
+
+    @property
+    def _chain_type(self) -> str:
+        return "graph_cypher_chain"
+
+    @classmethod
+    def from_llm(
+        cls,
+        llm: Optional[BaseLanguageModel] = None,
+        *,
+        qa_prompt: BasePromptTemplate = CYPHER_QA_PROMPT,
+        cypher_prompt: BasePromptTemplate = CYPHER_GENERATION_PROMPT,
+        cypher_llm: Optional[BaseLanguageModel] = None,
+        qa_llm: Optional[BaseLanguageModel] = None,
+        exclude_types: List[str] = [],
+        include_types: List[str] = [],
+        **kwargs: Any,
+    ) -> GraphCypherQAChain:
+        """Initialize from LLM."""
+
+        if not cypher_llm and not llm:
+            raise ValueError("Either `llm` or `cypher_llm` parameters must be provided")
+        if not qa_llm and not llm:
+            raise ValueError("Either `llm` or `qa_llm` parameters must be provided")
+        if cypher_llm and qa_llm and llm:
+            raise ValueError(
+                "You can specify up to two of 'cypher_llm', 'qa_llm'"
+                ", and 'llm', but not all three simultaneously."
+            )
+
+        qa_chain = LLMChain(llm=qa_llm or llm, prompt=qa_prompt)
+        cypher_generation_chain = LLMChain(llm=cypher_llm or llm, prompt=cypher_prompt)
+
+        if exclude_types and include_types:
+            raise ValueError(
+                "Either `exclude_types` or `include_types` "
+                "can be provided, but not both"
+            )
+
+        graph_schema = construct_schema(
+            kwargs["graph"].structured_schema, include_types, exclude_types
+        )
+
+        return cls(
+            graph_schema=graph_schema,
+            qa_chain=qa_chain,
+            cypher_generation_chain=cypher_generation_chain,
+            **kwargs,
+        )
+
+    def _call(
+        self,
+        inputs: Dict[str, Any],
+        run_manager: Optional[CallbackManagerForChainRun] = None,
+    ) -> Dict[str, Any]:
+        """Generate Cypher statement, use it to look up in db and answer question."""
+        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
+        callbacks = _run_manager.get_child()
+
+        intermediate_steps: List = []
+
+        generated_cypher = self.cypher_generation_chain.run(
+            {**inputs, "schema": self.graph_schema}, callbacks=callbacks
+        )
+
+        # Extract Cypher code if it is wrapped in backticks
+        generated_cypher = extract_cypher(generated_cypher)
+
+        _run_manager.on_text("Generated Cypher:", end="\n", verbose=self.verbose)
+        _run_manager.on_text(
+            generated_cypher, color="green", end="\n", verbose=self.verbose
+        )
+
+        intermediate_steps.append({"query": generated_cypher})
+
+        context = []
+        for statement in generated_cypher.split(';'):
+            if statement:
+                # Retrieve and limit the number of results
+                context += self.graph.query(statement)[: self.top_k]
+
+        if self.return_direct:
+            final_result = context
+        else:
+            _run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
+            _run_manager.on_text(
+                str(context), color="green", end="\n", verbose=self.verbose
+            )
+
+            intermediate_steps.append({"context": context})
+
+            result = self.qa_chain(
+                {**inputs, "context": context},
+                callbacks=callbacks,
+            )
+            final_result = result[self.qa_chain.output_key]
+
+        chain_result: Dict[str, Any] = {self.output_key: final_result}
+        if self.return_intermediate_steps:
+            chain_result[INTERMEDIATE_STEPS_KEY] = intermediate_steps
+
+        return chain_result
diff --git a/llm/llmlib/utils/chains/graph_query.py b/llm/llmlib/utils/chains/graph_query.py
new file mode 100644
index 0000000000..924d1176fc
--- /dev/null
+++ b/llm/llmlib/utils/chains/graph_query.py
@@ -0,0 +1,78 @@
+from typing import Any, Dict, List, Optional, TypeVar, Generic
+
+from langchain.callbacks.manager import CallbackManagerForChainRun
+from langchain.pydantic_v1 import Field
+from langchain.chains.base import Chain
+from langchain.schema import BaseOutputParser
+
+Graph = TypeVar("Graph")
+
+
+class GraphQueryChain(Chain, Generic[Graph]):
+    """Chain for converting terms to eid mappings"""
+
+    graph: Graph = Field(exclude=True)
+    graph_exception: Exception = Field(exclude=True, default=Exception)
+    aliased_input_keys: List[str] = Field(
+        alias='input_keys', default_factory=lambda: []
+    )
+    output_key: str = "results"  #: :meta private:
+    output_parser: BaseOutputParser = None
+
+    # Query to get term eids, input_keys are avaliable as query params
+    query: str
+
+    @property
+    def input_keys(self) -> List[str]:
+        return self.aliased_input_keys
+
+    @property
+    def output_keys(self) -> List[str]:
+        return [self.output_key]
+
+    def create_outputs(self, result: List[dict]) -> Dict[str, Any]:
+        """Create outputs from response."""
+        return {
+            self.output_key: self.output_parser.parse_result(result)
+            if self.output_parser
+            else result
+        }
+
+    def _call(
+        self,
+        inputs: Dict[str, Any],
+        run_manager: Optional[CallbackManagerForChainRun] = None,
+    ) -> Dict[str, Any]:
+        """
+        Users can modify the following FindEidsChain Class Variables:
+
+        :var top_k: The maximum number of AQL Query Results to return
+        :type top_k: int
+        """
+        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
+
+        graph_result = []
+
+        #####################
+        # Execute Query #
+
+        try:
+            graph_result = self.graph.query(self.query, inputs)
+        except self.graph_exception as e:
+            _run_manager.on_text(
+                "Graph Query Execution Error: ", end="\n", verbose=self.verbose
+            )
+            _run_manager.on_text(e, color="yellow", end="\n\n", verbose=self.verbose)
+        else:
+            _run_manager.on_text("Graph Result:", end="\n", verbose=self.verbose)
+            _run_manager.on_text(
+                str(graph_result), color="green", end="\n", verbose=self.verbose
+            )
+        #####################
+
+        # Return results #
+        return self.create_outputs(graph_result)
+
+    @property
+    def _chain_type(self) -> str:
+        return "graph_query_chain"
diff --git a/llm/llmlib/utils/chains/graph_retrieval_qa.py b/llm/llmlib/utils/chains/graph_retrieval_qa.py
new file mode 100644
index 0000000000..27da426cb3
--- /dev/null
+++ b/llm/llmlib/utils/chains/graph_retrieval_qa.py
@@ -0,0 +1,40 @@
+from typing import List
+
+from langchain.callbacks.manager import (
+    AsyncCallbackManagerForChainRun,
+    CallbackManagerForChainRun,
+)
+from langchain.chains.retrieval_qa.base import RetrievalQA
+from langchain.schema import BaseRetriever, Document
+from pydantic import Field
+
+
+class GraphRetrievalQA(RetrievalQA):
+    retriever: BaseRetriever = Field(exclude=True)
+
+    def _get_docs(
+        self,
+        question: str,
+        *,
+        run_manager: CallbackManagerForChainRun,
+    ) -> List[Document]:
+        """Get docs."""
+        return self.retriever.get_relevant_documents(
+            question, callbacks=run_manager.get_child()
+        )
+
+    async def _aget_docs(
+        self,
+        question: str,
+        *,
+        run_manager: AsyncCallbackManagerForChainRun,
+    ) -> List[Document]:
+        """Get docs."""
+        return await self.retriever.aget_relevant_documents(
+            question, callbacks=run_manager.get_child()
+        )
+
+    @property
+    def _chain_type(self) -> str:
+        """Return the chain type."""
+        return "retrieval_qa"
diff --git a/llm/llmlib/utils/config.py b/llm/llmlib/utils/config.py
new file mode 100644
index 0000000000..478012b022
--- /dev/null
+++ b/llm/llmlib/utils/config.py
@@ -0,0 +1,4 @@
+from flask import current_app
+from werkzeug.local import LocalProxy
+
+config = LocalProxy(lambda: current_app.config)
diff --git a/llm/llmlib/utils/dataclass.py b/llm/llmlib/utils/dataclass.py
new file mode 100644
index 0000000000..db1bffc491
--- /dev/null
+++ b/llm/llmlib/utils/dataclass.py
@@ -0,0 +1,108 @@
+from dataclasses import MISSING, fields
+from typing import Generic, Callable, overload, Any, TypeVar, Union
+
+Obj = TypeVar('Obj')
+T = TypeVar('T')
+
+
+class Descriptor(Generic[Obj, T]):
+    _property_prefix: str
+    _name: str
+
+    def __init__(self, property_prefix: str = '_descriptor_'):
+        self._property_prefix = property_prefix
+
+    def __set_name__(self, owner, name):
+        self._name = name
+
+    @property
+    def _prefixed_name(self):
+        return self._property_prefix + self._name
+
+    @overload
+    def __get__(self, instance: None, owner: type[Obj]) -> T:
+        """This is happening when dataclass is being created"""
+        ...
+
+    @overload
+    def __get__(self, instance: Obj, owner: type[Obj]) -> T:
+        ...
+
+    def __get__(self, instance: Union[Obj, None], owner: type[Obj]) -> T:
+        return getattr(instance, self._prefixed_name, MISSING)  # type: ignore
+
+    def _update_value(self, instance: Obj, value: T):
+        setattr(instance, self._prefixed_name, value)
+
+    def __set__(self, instance: Obj, value):
+        if value is not self:
+            # Dealing with frozen instances (once we read|write the value should be consider frozen)
+            if hasattr(instance, self._prefixed_name):
+                setattr(instance, self._prefixed_name, value)
+            else:
+                object.__setattr__(instance, self._prefixed_name, value)
+
+    def __repr__(self):
+        return f'<{type(self).__name__} {self._name}>'
+
+
+class LazyDefaulDescriptor(Descriptor[Obj, T]):
+    """Decriptor which calls default_factory only when the value is not set yet upon first read
+    Note: It can be used within frozen dataclassses
+    WARNING: Be very carefull while debugging this descriptor.
+     Revealing the property in the debugger will cause the descriptor to be called,
+     moreover during such call all debugging points will be suppressed.
+    """
+
+    _default_factory: Callable[[Obj, str], T]
+
+    def __init__(
+        self, default_factory: Callable[[Obj, str], T], property_prefix: str = '_lazy_'
+    ):
+        super().__init__(property_prefix=property_prefix)
+        self._default_factory = default_factory
+
+    def __get__(self, instance: Union[Obj, None], owner: Any) -> T:
+        # If we are accessing the descriptor from the dataclass wrapper, return the descriptor
+        if instance is None:
+            return self  # type: ignore
+
+        # If the value is already set, return it
+        set_value = getattr(instance, self._prefixed_name, MISSING)
+        if set_value is not MISSING:
+            return set_value
+
+        # If the value is not set, create it and set it
+        default = self._default_factory(instance, self._name)
+        object.__setattr__(instance, self._prefixed_name, default)
+        return default
+
+
+class CauseDefaultingDescriptor(LazyDefaulDescriptor[Any, T]):
+    """Descriptor which returns default value from the exception __cause__ if it exists
+    If running on a dataclass, it will try to match the type of the field
+    """
+
+    def __init__(
+        self,
+        default: T = MISSING,  # type: ignore
+        prefix: str = '_cause_',
+        replace_self: bool = False,
+    ):
+        def _default_factory(instance: Any, prop: str) -> T:
+            cause = getattr(instance, '__cause__', MISSING)
+            if cause is not MISSING:
+                cause_value = getattr(cause, prop, MISSING)
+                if cause_value is not MISSING:
+                    try:
+                        for f in fields(instance):
+                            if f.name == prop:
+                                if isinstance(cause_value, f.type):
+                                    return cause_value
+                                break
+                    except TypeError:
+                        # Running on a non dataclass
+                        return cause_value
+            return default
+
+        super().__init__(default_factory=_default_factory, property_prefix=prefix)
diff --git a/llm/llmlib/utils/lazy_neo4j_graph.py b/llm/llmlib/utils/lazy_neo4j_graph.py
new file mode 100644
index 0000000000..88052923cc
--- /dev/null
+++ b/llm/llmlib/utils/lazy_neo4j_graph.py
@@ -0,0 +1,32 @@
+from functools import cache
+
+from langchain.graphs import Neo4jGraph
+
+
+class LazyNeo4jGraph(Neo4jGraph):
+    """
+    A lazy Neo4j graph wrapper that only refreshes the schema when it is accessed.
+    This allows for custom schema to be set without heavy call to the database.
+    """
+
+    @cache
+    def lazy_refresh_schema(self) -> None:
+        """
+        Refreshes the Neo4j graph schema information.
+        """
+        return super().refresh_schema()
+
+    def refresh_schema(self) -> None:
+        pass
+
+    _schema: str
+
+    @property
+    def schema(self):
+        if self._schema is None:
+            self.lazy_refresh_schema()
+        return self._schema
+
+    @schema.setter
+    def schema(self, value):
+        self._schema = value
diff --git a/llm/llmlib/utils/output_parsers/graph_results.py b/llm/llmlib/utils/output_parsers/graph_results.py
new file mode 100644
index 0000000000..414ec39ac3
--- /dev/null
+++ b/llm/llmlib/utils/output_parsers/graph_results.py
@@ -0,0 +1,26 @@
+from langchain.output_parsers.list import ListOutputParser
+from typing import List, Dict, Any
+
+
+class GraphResultsOutputParser(ListOutputParser):
+    """Parse the output of a graph query to a list."""
+
+    key: str = 'output'
+    separator: str = '\n'
+
+    def get_format_instructions(self) -> str:
+        return (
+            "Your response should be a list of results, "
+            "eg: `[{key:<result1>}, {key:<result2>}]`"
+        )
+
+    @property
+    def _type(self) -> str:
+        return "graph-result-list"
+
+    def parse_result(self, result: List[Dict[str, Any]]) -> List[str]:
+        """Parse the output of a graph query."""
+        return self.parse(self.separator.join(map(lambda row: row[self.key], result)))
+
+    def parse(self, text: str) -> str:
+        return text
diff --git a/llm/llmlib/utils/retrievers/graph_search_retriever.py b/llm/llmlib/utils/retrievers/graph_search_retriever.py
new file mode 100644
index 0000000000..3888b468b4
--- /dev/null
+++ b/llm/llmlib/utils/retrievers/graph_search_retriever.py
@@ -0,0 +1,109 @@
+from typing import Optional, Self, List, Any
+
+from langchain import LLMChain, PromptTemplate, BasePromptTemplate
+from langchain.callbacks.manager import CallbackManagerForRetrieverRun
+from langchain.chat_models.base import BaseChatModel
+from langchain.output_parsers import CommaSeparatedListOutputParser
+from langchain.prompts import (
+    ChatPromptTemplate,
+    SystemMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+)
+from langchain.pydantic_v1 import Field
+from langchain.schema import BaseRetriever, Document
+from llmlib.utils.search.graph_search_api_wrapper import GraphSearchAPIWrapper
+
+
+def default_terms_prompt_factory(query_key: str):
+    return ChatPromptTemplate.from_messages(
+        [
+            SystemMessagePromptTemplate(
+                prompt=PromptTemplate(
+                    template="Always response with comma separated list of terms identified in prompt.",
+                    input_variables=[],
+                )
+            ),
+            HumanMessagePromptTemplate(
+                prompt=PromptTemplate(
+                    template=f"{{{query_key}}}",
+                    input_variables=[query_key],
+                )
+            ),
+        ]
+    )
+
+
+class GraphSearchRetriever(BaseRetriever):
+    llm_chain: LLMChain
+    graph_search: GraphSearchAPIWrapper = Field(
+        ..., description="Graph search API wrapper."
+    )
+    verbose: bool = False
+    relationships: List[Any] = []
+
+    @classmethod
+    def from_llm(
+        cls,
+        llm: BaseChatModel,
+        graph_search: GraphSearchAPIWrapper,
+        prompt: Optional[BasePromptTemplate] = None,
+        **kwargs: Any,
+    ) -> Self:
+        """Initialize from LLM using the default template."""
+        if not prompt:
+            prompt = default_terms_prompt_factory(cls.query_key)
+
+        llm_chain = LLMChain(
+            llm=llm,
+            prompt=prompt,
+            output_parser=CommaSeparatedListOutputParser(),
+        )
+
+        return cls(
+            llm_chain=llm_chain,
+            graph_search=graph_search,
+            **kwargs,
+        )
+
+    def _get_relevant_documents(
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
+    ) -> List[Document]:
+        """Get relevant documents from the graph."""
+
+        def sub_text(text, verbose=self.verbose, end='\n', **kwargs):
+            return run_manager.on_text(
+                '\t' + text.replace('\n', '\n\t'), verbose=verbose, end=end, **kwargs
+            )
+
+        def sub_header(text, verbose=self.verbose, end='\n', **kwargs):
+            return run_manager.on_text(
+                '\n\t\033[1m>> ' + text.replace('\n', '\n\t') + '\033[0m',
+                verbose=verbose,
+                end=end,
+                **kwargs,
+            )
+
+        # Get search terms
+        sub_header("Generating list of revelant terms")
+        inputs = self.llm_chain.prep_inputs({"query": query})
+        output = self.llm_chain(inputs)
+        terms = output['text']
+        sub_text(f"Identified Terms:\n{terms}")
+
+        sub_header("Searching for related graph nodes")
+        nodes = self.graph_search.get_related_nodes(terms)
+        sub_text(f"Identified Nodes:\n{nodes}")
+
+        if len(nodes) > 1:
+            sub_header("Searching for related graph relationships\n")
+            relationships = self.graph_search.get_relationships(nodes)
+            sub_text(f"Identified Relationships:\n{relationships}\n")
+            self.relationships = relationships
+
+            if len(relationships) > 0:
+                return [
+                    self.graph_search.relationship_to_document(relationship)
+                    for relationship in relationships
+                ]
+
+        return [self.graph_search.node_to_document(node) for node in nodes]
diff --git a/llm/llmlib/utils/search/cypher_search_api_wrapper.py b/llm/llmlib/utils/search/cypher_search_api_wrapper.py
new file mode 100644
index 0000000000..5540c5c233
--- /dev/null
+++ b/llm/llmlib/utils/search/cypher_search_api_wrapper.py
@@ -0,0 +1,173 @@
+from typing import List, Any, Dict, TypedDict
+
+import yaml
+from langchain.schema import Document
+from llmlib.utils.lazy_neo4j_graph import LazyNeo4jGraph
+from llmlib.utils.search.graph_search_api_wrapper import GraphSearchAPIWrapper
+from math import floor
+
+CURRENCY_METABOLITES = [
+    'NAD-P-OR-NOP', 'NADH-P-OR-NOP', 'Donor-H2', 'Acceptor', 'HYDROGEN-PEROXIDE', 'OXYGEN-MOLECULE',
+    'NAD', 'NADP', 'NADH', 'NADPH', 'WATER', 'CARBON-DIOXIDE', 'FAD', 'CO-A', 'UDP', 'AMMONIA',
+    'NA+',
+    'AMMONIUM', 'PROTON', 'CARBON-MONOXIDE', 'GTP', 'ADP', 'GDP', 'AMP', 'ATP', '3-5-ADP', 'PPI',
+    'Pi'
+]
+
+RANKED_RELATED_NODES_MAPPING_QUERY = """
+UNWIND $terms AS term
+MATCH (s:Synonym {lowercase_name: toLower(term)})<-[:HAS_SYNONYM]-(n)
+WHERE n.eid IS NOT NULL AND NOT n.eid IN $exclude_eids
+WITH apoc.text.levenshteinSimilarity(term, n.name) as similarity, s, n, term
+ORDER BY similarity DESC
+WHERE similarity > 0.5
+WITH DISTINCT term, collect({node: n, node_id: id(n), similarity: similarity})[0..$top_k_matches] as matches
+RETURN term, matches
+"""
+
+RELATED_SHORTEST_PATH_QUERY = """
+UNWIND apoc.coll.combinations($term_match, 2) AS termPair
+UNWIND termPair[0]['matches'] as matchA
+UNWIND termPair[1]['matches'] as matchB
+WITH matchA, matchB, matchA['similarity'] * matchB['similarity'] AS similarity
+ORDER BY similarity DESC
+WHERE similarity > 0.5
+MATCH (a), (b)
+WHERE id(a) = matchA['id'] AND id(b) = matchB['id']
+MATCH path=allShortestPaths((a)-[*1..]-(b))
+WHERE
+ all(r in relationships(path) where type(r) in $include_rels)
+ and all(n in nodes(path) where not n.eid in $exclude_eids)
+RETURN path LIMIT $top_k
+"""
+
+
+class Node(TypedDict):
+    eid: str
+
+
+class NodeMatch(TypedDict):
+    node: Node
+    node_id: int
+    similarity: float
+
+
+class TermMatches(TypedDict):
+    term: str
+    matches: List[NodeMatch]
+
+
+def preformat_entity(entity):
+    return {
+        k: v
+        for k, v in entity.items()
+        if v and (
+            k not in (
+                'genes', 'inchi', 'inchi_key', 'original_entity_types', 'left_end_position',
+                'right_end_position',
+            )
+        )
+    }
+
+
+def term_match_template(obj):
+    return yaml.dump(
+        {
+            obj['term']: [
+                {
+                    'matches': [
+                        preformat_entity(match['node'])
+                        for match in obj['matches']
+                    ]
+                }
+            ]
+        }
+    )
+
+
+def relationship_template(obj):
+    return yaml.dump(
+        {
+            'relationship': {
+                'path': ' - '.join(
+                    map(
+                        lambda n: n.get('eid', '') if isinstance(n, dict) else n,
+                        obj['path'],
+                    )
+                ),
+                'segments': [
+                    (preformat_entity(n) if isinstance(n, dict) else n)
+                    for n in obj['path']
+                ]
+            }
+        }
+    )
+
+
+class CypherSearchAPIWrapper(GraphSearchAPIWrapper):
+    graph: LazyNeo4jGraph
+
+    class Config:
+        arbitrary_types_allowed = True
+
+    related_nodes_query: str = RANKED_RELATED_NODES_MAPPING_QUERY
+    related_nodes_query_params: Dict[str, Any] = dict(
+        top_k_matches=5,
+        exclude_eids=CURRENCY_METABOLITES,
+    )
+    relationships_query: str = RELATED_SHORTEST_PATH_QUERY
+    relationships_query_params: Dict[str, Any] = dict(
+        top_k=3,
+        include_rels=['CONSUMED_BY', 'PRODUCES', 'CATALYZES', 'REGULATES', 'HAS_TXONOMY', 'IS',
+                      'HAS_KO', 'ELEMENT_OF', 'ENCODES'],
+        exclude_eids=CURRENCY_METABOLITES,
+    )
+
+    def node_to_document(self, term_match: TermMatches) -> Document:
+        return Document(
+            page_content=term_match_template(term_match),
+            metadata=dict(
+                term=term_match['term'],
+            ),
+        )
+
+    def relationship_to_document(self, relationship: dict) -> Document:
+        path_len = len(relationship['path'])
+        return Document(
+            page_content=relationship_template(relationship),
+            metadata=dict(
+                start_eid=relationship['path'][0]['eid'],
+                end_eid=relationship['path'][path_len - 1]['eid'],
+                distance=floor(path_len / 2)
+            ),
+        )
+
+    def get_related_nodes(self, terms: List[str]) -> List[TermMatches]:
+        mapping = self.graph.query(
+            self.related_nodes_query,
+            dict(**self.related_nodes_query_params, terms=terms),
+        )
+        assert len(mapping) <= len(terms)
+        return mapping
+
+    def get_relationships(self, term_match: List[TermMatches]) -> List[dict]:
+        term_match_id = [
+            {
+                'term': term_match['term'],
+                'matches': [
+                    {
+                        'id': match['node_id'],
+                        'similarity': match['similarity'],
+                    }
+                    for match in term_match['matches']
+                ],
+            }
+            for term_match in term_match
+        ]
+
+        return self.graph.query(
+            self.relationships_query,
+            dict(
+            **self.relationships_query_params, term_match=term_match_id
+            ),
+        )
diff --git a/llm/llmlib/utils/search/graph_search_api_wrapper.py b/llm/llmlib/utils/search/graph_search_api_wrapper.py
new file mode 100644
index 0000000000..3f848e43e1
--- /dev/null
+++ b/llm/llmlib/utils/search/graph_search_api_wrapper.py
@@ -0,0 +1,28 @@
+from abc import ABC, abstractmethod
+from typing import List, TypeVar, Generic
+
+from langchain.schema import Document
+from pydantic.v1 import BaseModel
+
+NODE = TypeVar("NODE")
+RELATIONSHIP = TypeVar("RELATIONSHIP")
+
+
+class GraphSearchAPIWrapper(ABC, Generic[NODE, RELATIONSHIP], BaseModel):
+    """Abstract class for graph search API wrappers."""
+
+    @abstractmethod
+    def node_to_document(self, node: NODE) -> Document:
+        ...
+
+    @abstractmethod
+    def relationship_to_document(self, relationship: RELATIONSHIP) -> Document:
+        ...
+
+    @abstractmethod
+    def get_related_nodes(self, terms: List[str]) -> List[NODE]:
+        ...
+
+    @abstractmethod
+    def get_relationships(self, nodes: List[NODE]) -> List[RELATIONSHIP]:
+        ...
diff --git a/llm/llmlib/utils/singleton.py b/llm/llmlib/utils/singleton.py
new file mode 100644
index 0000000000..8f026e07d4
--- /dev/null
+++ b/llm/llmlib/utils/singleton.py
@@ -0,0 +1,13 @@
+from abc import ABCMeta
+
+
+class Singleton(ABCMeta):
+    """Singleton metaclass."""
+
+    _instances = {}
+
+    def __call__(cls, *args, **kwargs):
+        """Return singleton instance."""
+        if cls not in cls._instances:
+            cls._instances[cls] = super().__call__(*args, **kwargs)
+        return cls._instances[cls]
diff --git a/llm/llmlib/utils/string.py b/llm/llmlib/utils/string.py
new file mode 100644
index 0000000000..d15ff0c2de
--- /dev/null
+++ b/llm/llmlib/utils/string.py
@@ -0,0 +1,27 @@
+import itertools
+import re
+import sys
+from enum import Enum
+from enum import EnumMeta
+from json import JSONDecodeError
+from string import punctuation, whitespace
+from typing import Iterator, Dict
+
+import attr
+import unicodedata
+from _decimal import Decimal, InvalidOperation
+from flask import json
+
+
+def indent_lines(*lines: str, level=1, indent_string: str = '\t'):
+    return list(map(lambda s: indent_string * level + s, lines))
+
+
+def compose_lines(*lines: str, new_line_delimeter: str = '\n'):
+    return new_line_delimeter.join(lines)
+
+
+__all__ = [
+    'indent_lines',
+    'compose_lines',
+]
diff --git a/llm/llmlib/utils/transaction_id.py b/llm/llmlib/utils/transaction_id.py
new file mode 100644
index 0000000000..835da7294e
--- /dev/null
+++ b/llm/llmlib/utils/transaction_id.py
@@ -0,0 +1,6 @@
+from flask import g
+from werkzeug.local import LocalProxy
+
+transaction_id = LocalProxy(
+    lambda: getattr(g, 'transaction_id', 'call_from_outside_of_request_scope')
+)
diff --git a/llm/llmlib/v0/__init__.py b/llm/llmlib/v0/__init__.py
new file mode 100644
index 0000000000..485077561b
--- /dev/null
+++ b/llm/llmlib/v0/__init__.py
@@ -0,0 +1,101 @@
+from typing import List, TypedDict, Union
+
+from langchain import PromptTemplate
+from langchain.callbacks import StdOutCallbackHandler
+from langchain.chains import RetrievalQA
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import (
+    ChatPromptTemplate,
+    SystemMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+)
+from llmlib.utils.retrievers.graph_search_retriever import GraphSearchRetriever
+from llmlib.utils.search.cypher_search_api_wrapper import CypherSearchAPIWrapper
+
+core_terms_prompt_template = ChatPromptTemplate.from_messages(
+    [
+        SystemMessagePromptTemplate(
+            prompt=PromptTemplate(
+                template="Always response with comma separated list of chemical or biological terms identified in prompt.",
+                input_variables=[],
+            )
+        ),
+        HumanMessagePromptTemplate(
+            prompt=PromptTemplate(
+                template="{query}",
+                input_variables=["query"],
+            )
+        ),
+    ]
+)
+handler = StdOutCallbackHandler()
+qa_system_template = """Use the following pieces of context to answer the users question. Each piece of information repsesents uncorelated cases, responses based on different pieces shoul be places into separate paragraphs.
+If you don't know the answer, just say that you don't know, don't try to make up an answer.
+Work on the answear in two steps:
+1. Rephase pieces of context ussing natural language
+2. Based on repharsed text respond to the question
+----------------
+{context}
+----------------"""
+qa_prompt_template = ChatPromptTemplate.from_messages(
+    [
+        SystemMessagePromptTemplate.from_template(qa_system_template),
+        HumanMessagePromptTemplate.from_template("{question}"),
+    ]
+)
+
+
+class PathRow(TypedDict):
+    path: List[Union[str, dict]]
+
+
+def paths_to_graph(paths: List[PathRow]):
+    nodes = {}
+    edges = []
+    for path_row in paths:
+        path = path_row["path"]
+        for index, segment in enumerate(path):
+            if isinstance(segment, dict):
+                nodes[segment["eid"]] = segment
+            else:
+                edges.append(
+                    {
+                        'from': path[index - 1]["eid"],
+                        'to': path[index + 1]["eid"],
+                        'label': segment,
+                    }
+                )
+
+    return dict(
+        nodes=list(nodes.values()),
+        edges=edges,
+    )
+
+
+def graph_qa_v0(query: str, graph, **kwargs):
+    """Query the LLM for a given text."""
+    llm = ChatOpenAI(**{**dict(temperature=0, verbose=True), **kwargs})  # defaults
+    search = CypherSearchAPIWrapper(
+        graph=graph, verbose=True, return_intermediate_steps=True
+    )
+    # noinspection PyTypeChecker
+    retriever = GraphSearchRetriever.from_llm(
+        llm=llm,
+        graph_search=search,
+        prompt=core_terms_prompt_template,
+        verbose=True,
+        return_intermediate_steps=True,
+        callbacks=[handler],
+    )
+    qa = RetrievalQA.from_chain_type(
+        llm=llm,
+        chain_type_kwargs=dict(prompt=qa_prompt_template),
+        retriever=retriever,
+        verbose=True,
+        callbacks=[handler],
+    )
+
+    return dict(
+        result=qa(query),
+        graph=paths_to_graph(retriever.relationships),
+    )
diff --git a/llm/llmlib/views.py b/llm/llmlib/views.py
new file mode 100644
index 0000000000..82ae519703
--- /dev/null
+++ b/llm/llmlib/views.py
@@ -0,0 +1,88 @@
+from typing import Union
+
+from flask import g
+from langchain import OpenAI
+from langchain.chat_models import ChatOpenAI
+from langchain.graphs import Neo4jGraph, ArangoGraph
+from llmlib.schemas import GraphQARequestSchema
+from webargs.flaskparser import use_args
+from .app import app
+from .database import Arango, Neo4j
+from .interfaces import GraphRef
+from .schemas import GraphCompletionsRequestSchema, GraphChatCompletionsRequestSchema
+
+
+@app.route('/', methods=['GET', 'POST'])
+def enrich():
+    raise Exception('No function provided!')
+
+
+@app.route('/healthz', methods=['GET', 'POST'])
+def healthz():
+    return "I am OK!"
+
+
+def map_graph_ref(graph: GraphRef) -> Union[ArangoGraph, Neo4jGraph]:
+    database_type = graph['database_type']
+    if database_type == 'arango':
+        database = Arango
+    elif database_type == 'neo4j':
+        database = Neo4j
+    else:
+        raise Exception('Invalid database type!')
+    database_name = graph.get('database_name')
+    return database().graph(database_name) if database_name else database().graph()
+
+
+def map_request_arguments(args):
+    model_kwargs = dict()
+    if 'graph' in args:
+        model_kwargs['graph'] = map_graph_ref(args['graph'])
+    if 'temperature' in args:
+        model_kwargs['temperature'] = args['temperature']
+    if 'top_p' in args:
+        model_kwargs['top_p'] = args['top_p']
+    if 'frequency_penalty' in args:
+        model_kwargs['frequency_penalty'] = args['frequency_penalty']
+    if 'presence_penalty' in args:
+        model_kwargs['presence_penalty'] = args['presence_penalty']
+    if 'stop' in args:
+        model_kwargs['stop'] = args['stop']
+    if 'n' in args:
+        model_kwargs['n'] = args['n']
+    if 'logit_bias' in args:
+        model_kwargs['logit_bias'] = args['logit_bias']
+    if 'logprobs' in args:
+        model_kwargs['logprobs'] = args['logprobs']
+    if 'stream' in args:
+        model_kwargs['stream'] = args['stream']
+    if 'max_tokens' in args:
+        model_kwargs['max_tokens'] = args['max_tokens']
+    if 'timeout' in args:
+        model_kwargs['timeout'] = args['timeout']
+    if 'user' in args:
+        model_kwargs['user'] = args['user']
+    return model_kwargs
+
+
+@app.route('/completions', methods=['POST'])
+@use_args(GraphCompletionsRequestSchema)
+def completions(args):
+    getattr(g, 'transaction_id', args['transaction_id'])
+    return OpenAI(**map_request_arguments(args))
+
+
+@app.route('/chat', methods=['POST'])
+@use_args(GraphChatCompletionsRequestSchema)
+def chat(args):
+    getattr(g, 'transaction_id', args['transaction_id'])
+    return ChatOpenAI(**map_request_arguments(args))
+
+
+@app.route('/graph', methods=['POST'])
+@use_args(GraphQARequestSchema)
+def graph(args):
+    getattr(g, 'transaction_id', args['transaction_id'])
+    from llmlib.v0 import graph_qa_v0
+
+    return graph_qa_v0(args['query'], **map_request_arguments(args))
diff --git a/llm/makefile b/llm/makefile
new file mode 100644
index 0000000000..015b1571f2
--- /dev/null
+++ b/llm/makefile
@@ -0,0 +1,17 @@
+prettify:
+	docker run --rm -v .:/apps alpine/flake8:6.0.0 .
+
+lint:
+	docker run --rm -v .:/data cytopia/pycodestyle:2 .
+
+dev:
+	docker compose\
+		-f ../docker-compose.yml\
+		-f ../docker-compose.override.yml\
+		run --rm -p 8888:8888 llm ./bin/dev.sh
+
+lock:
+	docker run --rm -v $$(pwd)/:/home/user $$(docker build -q --target lock .) pipenv lock
+
+test:
+	docker run --rm \$$\(docker build -q --target dev .\) pytest tests
diff --git a/llm/setup.cfg b/llm/setup.cfg
new file mode 100644
index 0000000000..788d8dabca
--- /dev/null
+++ b/llm/setup.cfg
@@ -0,0 +1,26 @@
+[pycodestyle]
+max-line-length = 100
+ignore = E203
+exclude = .local/,.svn,CVS,.bzr,.hg,.git
+
+[mypy]
+follow_imports = silent
+ignore_missing_imports = true
+
+[flake8]
+max-line-length = 100
+select = F
+exclude = .local/,.svn,CVS,.bzr,.hg,.git
+extend-ignore =
+# for compability with black
+    E203,
+# 'X' imported but unused
+    F401,
+# from .X import *' used; unable to detect undefined names
+    F403,
+# 'X' may be undefined, or defined from star imports: .Y
+    F405,
+# f-string is missing placeholders
+    F541,
+# local variable 'X' is assigned to but never used
+    F841
diff --git a/llm/setup.py b/llm/setup.py
new file mode 100644
index 0000000000..d1bc91a13f
--- /dev/null
+++ b/llm/setup.py
@@ -0,0 +1,7 @@
+from setuptools import setup, find_packages
+
+setup(
+    name="llmlib",
+    version="0.1",
+    packages=find_packages(),
+)
diff --git a/llm/tests/test_queries.py b/llm/tests/test_queries.py
new file mode 100644
index 0000000000..dc31fe869d
--- /dev/null
+++ b/llm/tests/test_queries.py
@@ -0,0 +1,66 @@
+from langchain.callbacks import StdOutCallbackHandler
+from langchain.chains import RetrievalQA
+from langchain.chat_models import ChatOpenAI
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.prompts import PromptTemplate
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    SystemMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+)
+from langchain.vectorstores import Chroma
+from llmlib.database import Neo4j
+from llmlib.utils.retrievers.graph_search_retriever import GraphSearchRetriever
+from llmlib.utils.search.cypher_search_api_wrapper import CypherSearchAPIWrapper
+
+from app import app
+
+QUERIES = (
+    "What is the relationship between Zn2+ and glycolate?",
+    "What is the relationship between INHBA and MTMR4?",
+)
+
+
+def test_queries():
+    with app.app_context():
+        llm = ChatOpenAI(temperature=0, verbose=True)
+        core_terms_prompt_template = ChatPromptTemplate.from_messages(
+            [
+                SystemMessagePromptTemplate(
+                    prompt=PromptTemplate(
+                        template="Always response with comma separated list of chemical or biological terms identified in prompt.",
+                        input_variables=[],
+                    )
+                ),
+                HumanMessagePromptTemplate(
+                    prompt=PromptTemplate(
+                        template="{query}",
+                        input_variables=["query"],
+                    )
+                ),
+            ]
+        )
+        embeddings = OpenAIEmbeddings()
+        vectorstore = Chroma(
+            embedding_function=embeddings, persist_directory="./chroma_db_oai"
+        )
+        graph = Neo4j().graph()
+        search = CypherSearchAPIWrapper(
+            graph=graph, verbose=True, return_intermediate_steps=True
+        )
+        handler = StdOutCallbackHandler()
+        retriever = GraphSearchRetriever.from_llm(
+            vectorstore=vectorstore,
+            llm=llm,
+            graph_search=search,
+            prompt=core_terms_prompt_template,
+            verbose=True,
+            return_intermediate_steps=True,
+            callbacks=[handler],
+        )
+        qa = RetrievalQA.from_chain_type(
+            llm=llm, retriever=retriever, verbose=True, callbacks=[handler]
+        )
+
+        for query in QUERIES:
+            print(qa.run(query))