Working but slow LynxScribe in LynxKite.

data/LynxScribe demo

@@ -0,0 +1,973 @@
+{
+  "env": "LynxScribe",
+  "nodes": [
+    {
+      "id": "Input chat 1",
+      "type": "basic",
+      "data": {
+        "title": "Input chat",
+        "params": {
+          "chat": "mi az elet titka"
+        },
+        "display": null,
+        "error": null,
+        "meta": {
+          "name": "Input chat",
+          "params": {
+            "chat": {
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+              "type": {
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+          "inputs": {},
+          "outputs": {
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+              "name": "output",
+              "type": {
+                "type": "None"
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+              "position": "right"
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+        "display": {
+          "dataframes": {
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+                "response"
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+              "data": [
+                [
+                  "Az élet titka sokak számára különböző lehet, és sok tényezőtől függ. Néhány kulcselem, ami segíthet megtalálni az élet értelmét vagy titkát:\n\n- **Kapcsolatok**: A barátok és a család közelsége fontos az érzelmi jólét szempontjából.\n- **Önmegvalósítás**: Mindenkinek más a célja és álma, érdemes dolgozni azon, hogy elérjük őket.\n- **Tanulás**: Folyamatosan fejlődjünk és tanuljunk, hogy jobban megértsük a világot.\n- **Egészség**: A fizikai és mentális egészség megőrzése kulcsfontosságú az életminőség szempontjából.\n- **Kibékülés**: Békélj meg a múltaddal, és tanulj meg megbocsátani önmagadnak és másoknak.\n\nEzek az elemek hozzájárulhatnak ahhoz, hogy az életet gazdagabbnak és értékesebbnek érezd. Van valami konkrét aspektus az élet titkáról, amiről szívesen beszélnél?\n\nPlease visit <a href='https://www.linkedin.com/in/g%c3%a1bor-benedek-95578717' target='_blank'>https://www.linkedin.com/in/g%c3%a1bor-benedek-95578717</a> for further information."
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+        }
+      },
+      "position": {
+        "x": -192.5486444668937,
+        "y": 1414.696184081429
+      },
+      "parentId": null
+    }
+  ],
+  "edges": [
+    {
+      "id": "xy-edge__Knowledge base 1output-Chat API 1knowledge_base",
+      "source": "Knowledge base 1",
+      "target": "Chat API 1",
+      "sourceHandle": "output",
+      "targetHandle": "knowledge_base"
+    },
+    {
+      "id": "xy-edge__RAG chatbot 1output-Chat API 1chatbot",
+      "source": "RAG chatbot 1",
+      "target": "Chat API 1",
+      "sourceHandle": "output",
+      "targetHandle": "chatbot"
+    },
+    {
+      "id": "xy-edge__LLM 1output-RAG chatbot 1llm",
+      "source": "LLM 1",
+      "target": "RAG chatbot 1",
+      "sourceHandle": "output",
+      "targetHandle": "llm"
+    },
+    {
+      "id": "xy-edge__Scenario selector 1output-RAG chatbot 1scenario_selector",
+      "source": "Scenario selector 1",
+      "target": "RAG chatbot 1",
+      "sourceHandle": "output",
+      "targetHandle": "scenario_selector"
+    },
+    {
+      "id": "xy-edge__RAG graph 1output-RAG chatbot 1rag_graph",
+      "source": "RAG graph 1",
+      "target": "RAG chatbot 1",
+      "sourceHandle": "output",
+      "targetHandle": "rag_graph"
+    },
+    {
+      "id": "xy-edge__Vector store 1output-RAG graph 1vector_store",
+      "source": "Vector store 1",
+      "target": "RAG graph 1",
+      "sourceHandle": "output",
+      "targetHandle": "vector_store"
+    },
+    {
+      "id": "xy-edge__Text embedder 2output-RAG graph 1text_embedder",
+      "source": "Text embedder 2",
+      "target": "RAG graph 1",
+      "sourceHandle": "output",
+      "targetHandle": "text_embedder"
+    },
+    {
+      "id": "xy-edge__LLM 2output-Text embedder 2llm",
+      "source": "LLM 2",
+      "target": "Text embedder 2",
+      "sourceHandle": "output",
+      "targetHandle": "llm"
+    },
+    {
+      "id": "xy-edge__Truncate history 1output-Chat processor 1processor",
+      "source": "Truncate history 1",
+      "target": "Chat processor 1",
+      "sourceHandle": "output",
+      "targetHandle": "processor"
+    },
+    {
+      "id": "xy-edge__Chat processor 1output-Chat API 1chat_processor",
+      "source": "Chat processor 1",
+      "target": "Chat API 1",
+      "sourceHandle": "output",
+      "targetHandle": "chat_processor"
+    },
+    {
+      "id": "xy-edge__Chat API 1output-Test Chat API 1chat_api",
+      "source": "Chat API 1",
+      "target": "Test Chat API 1",
+      "sourceHandle": "output",
+      "targetHandle": "chat_api"
+    },
+    {
+      "id": "xy-edge__Test Chat API 1output-View 1input",
+      "source": "Test Chat API 1",
+      "target": "View 1",
+      "sourceHandle": "output",
+      "targetHandle": "input"
+    },
+    {
+      "id": "xy-edge__Input chat 1output-Test Chat API 1message",
+      "source": "Input chat 1",
+      "target": "Test Chat API 1",
+      "sourceHandle": "output",
+      "targetHandle": "message"
+    },
+    {
+      "id": "xy-edge__Mask 1output-Chat processor 1processor",
+      "source": "Mask 1",
+      "target": "Chat processor 1",
+      "sourceHandle": "output",
+      "targetHandle": "processor"
+    },
+    {
+      "id": "xy-edge__Mask 2output-Chat processor 1processor",
+      "source": "Mask 2",
+      "target": "Chat processor 1",
+      "sourceHandle": "output",
+      "targetHandle": "processor"
+    }
+  ]
+}

server/executors/one_by_one.py

@@ -37,7 +37,7 @@ def register(env: str, cache: bool = True):
   ops.EXECUTORS[env] = lambda ws: execute(ws, ops.CATALOGS[env], cache=cache)
 
 def get_stages(ws, catalog):
-  '''Inputs on top are batch inputs. We decompose the graph into a DAG of components along these edges.'''
+  '''Inputs on top/bottom are batch inputs. We decompose the graph into a DAG of components along these edges.'''
   nodes = {n.id: n for n in ws.nodes}
   batch_inputs = {}
   inputs = {}
@@ -46,7 +46,7 @@ def get_stages(ws, catalog):
     node = nodes[edge.target]
     op = catalog[node.data.title]
     i = op.inputs[edge.targetHandle]
-    if i.position == 'top':
+    if i.position in 'top or bottom':
       batch_inputs.setdefault(edge.target, []).append(edge.source)
   stages = []
   for bt, bss in batch_inputs.items():
@@ -77,7 +77,7 @@ def execute(ws, catalog, cache=None):
     node.data.error = None
     op = catalog[node.data.title]
     # Start tasks for nodes that have no non-batch inputs.
-    if all([i.position == 'top' for i in op.inputs.values()]):
+    if all([i.position in 'top or bottom' for i in op.inputs.values()]):
       tasks[node.id] = [NO_INPUT]
   batch_inputs = {}
   # Run the rest until we run out of tasks.
@@ -99,7 +99,7 @@ def execute(ws, catalog, cache=None):
       for task in ts:
         try:
           inputs = [
-            batch_inputs[(n, i.name)] if i.position == 'top' else task
+            batch_inputs[(n, i.name)] if i.position in 'top or bottom' else task
             for i in op.inputs.values()]
           if cache:
             key = json.dumps(fastapi.encoders.jsonable_encoder((inputs, params)))
@@ -126,7 +126,7 @@ def execute(ws, catalog, cache=None):
           t = nodes[edge.target]
           op = catalog[t.data.title]
           i = op.inputs[edge.targetHandle]
-          if i.position == 'top':
+          if i.position in 'top or bottom':
             batch_inputs.setdefault((edge.target, edge.targetHandle), []).extend(results)
           else:
             tasks.setdefault(edge.target, []).extend(results)

server/lynxscribe_ops.py

@@ -1,52 +1,150 @@
-'''An example of passive ops. Just using LynxKite to describe the configuration of a complex system.'''
-from .ops import passive_op_registration, Parameter as P, MULTI_INPUT
-
-reg = passive_op_registration('LynxScribe')
-reg('Scrape documents', params=[P.basic('url', '')])
-reg('Conversation logs')
-reg('Extract graph', inputs=['input'])
-reg('Compute embeddings', inputs=['input'], params=[P.options('method', ['OpenAI', 'graph', 'Yi-34b']), P.basic('dimensions', 1234)])
-reg('Vector DB', inputs=[MULTI_INPUT], params=[P.options('backend', ['FAISS', 'ANN', 'HNSW'])])
-reg('Chat UI', outputs=[], inputs=['input'])
-reg('Chat backend')
-reg('WhatsApp')
-reg('PII removal', inputs=['input'])
-reg('Intent classification', inputs=['input'])
-reg('System prompt', params=[P.basic('prompt', 'You are a helpful chatbot.')])
-reg('LLM', inputs=[MULTI_INPUT], params=[P.options('backend', ['GPT-4', 'Yi-34b', 'Claude 3 Opus', 'Google Gemini'])])
-
-# From Marton's mock-up.
-yi = 'Yi-34B (triton)'
-reg('Chat Input', params=[
-  P.options('load_mode', ['augmented']),
-  P.options('model', [yi]),
-  P.options('embedder', ['GritLM-7b (triton)']),
-  ])
-reg('k-NN Intent Classifier', inputs=['qa_embs', 'rag_graph'], params=[
-  P.options('distance', ['cosine', 'euclidean']),
-  P.basic('max_dist', 0.3),
-  P.basic('k', 3),
-  P.options('voting', ['most common', 'weighted']),
-  ])
-reg('Chroma Graph RAG Loader', inputs=[], params=[
-  P.options('location', ['GCP']),
-  P.collapsed('bucket', ''),
-  P.collapsed('folder', ''),
-  P.options('embedder', ['GritLM-7b (triton)']),
-  ])
-reg('Scenario Builder', inputs=['input'], params=[
-  P.collapsed('scenario', ''),
-  ])
-reg('Graph RAG Answer', inputs=['qa_embs', 'intent', 'rag_graph', 'prompt_dict'], params=[
-  P.options('answer_llm', [yi]),
-  P.basic('faq_dist', 0.12),
-  P.basic('max_dist', 0.25),
-  P.basic('ctx_tokens', 2800),
-  P.options('distance', ['cosine', 'euclidean']),
-  P.collapsed('graph_rag_params', ''),
-  ])
-reg('Answer Post Processing', inputs=['qa_embs', 'rag_graph'], params=[
-  P.options('distance', ['cosine', 'euclidean']),
-  P.basic('min_conf', 0.78),
-  ])
-reg('Chat Output', inputs=['input'], outputs=[])
+"""
+LynxScribe configuration and testing in LynxKite.
+"""
+from lynxscribe.core.llm.base import get_llm_engine
+from lynxscribe.core.vector_store.base import get_vector_store
+from lynxscribe.common.config import load_config
+from lynxscribe.components.text_embedder import TextEmbedder
+from lynxscribe.components.rag.rag_graph import RAGGraph
+from lynxscribe.components.rag.knowledge_base_graph import PandasKnowledgeBaseGraph
+from lynxscribe.components.rag.rag_chatbot import Scenario, ScenarioSelector, RAGChatbot
+from lynxscribe.components.chat_processor.base import ChatProcessor
+from lynxscribe.components.chat_processor.processors import MaskTemplate, TruncateHistory
+from lynxscribe.components.chat_api import ChatAPI, ChatAPIRequest, ChatAPIResponse
+
+from . import ops
+import asyncio
+from .executors import one_by_one
+
+ENV = 'LynxScribe'
+one_by_one.register(ENV)
+op = ops.op_registration(ENV)
+
+@op("Vector store")
+def vector_store(*, name='chromadb', collection_name='lynx'):
+  vector_store = get_vector_store(name=name, collection_name=collection_name)
+  return {'vector_store': vector_store}
+
+@op("LLM")
+def llm(*, name='openai'):
+  llm = get_llm_engine(name=name)
+  return {'llm': llm}
+
+@ops.input_position(llm="bottom")
+@op("Text embedder")
+def text_embedder(llm, *, model='text-embedding-ada-002'):
+  llm = llm[0]['llm']
+  text_embedder = TextEmbedder(llm=llm, model=model)
+  return {'text_embedder': text_embedder}
+
+@ops.input_position(vector_store="bottom", text_embedder="bottom")
+@op("RAG graph")
+def rag_graph(vector_store, text_embedder):
+    vector_store = vector_store[0]['vector_store']
+    text_embedder = text_embedder[0]['text_embedder']
+    rag_graph = RAGGraph(
+        PandasKnowledgeBaseGraph(vector_store=vector_store, text_embedder=text_embedder)
+    )
+    return {'rag_graph': rag_graph}
+
+@op("Scenario selector")
+def scenario_selector(*, scenario_file: str, node_types='intent_cluster'):
+  scenarios = load_config(scenario_file)
+  node_types = [t.strip() for t in node_types.split(',')]
+  scenario_selector = ScenarioSelector(
+      scenarios=[Scenario(**scenario) for scenario in scenarios],
+      node_types=node_types,
+  )
+  return {'scenario_selector': scenario_selector}
+
+DEFAULT_NEGATIVE_ANSWER = "I'm sorry, but the data I've been trained on does not contain any information related to your question."
+
+@ops.input_position(rag_graph="bottom", scenario_selector="bottom", llm="bottom")
+@op("RAG chatbot")
+def rag_chatbot(
+    rag_graph, scenario_selector, llm, *,
+    negative_answer=DEFAULT_NEGATIVE_ANSWER,
+    min_information=2, max_information=3,
+    min_summary=2, max_summary=3,
+    strict_limits=True, max_results=5):
+  rag_graph = rag_graph[0]['rag_graph']
+  scenario_selector = scenario_selector[0]['scenario_selector']
+  llm = llm[0]['llm']
+  rag_chatbot = RAGChatbot(
+      rag_graph=rag_graph,
+      scenario_selector=scenario_selector,
+      llm=llm,
+      negative_answer=negative_answer,
+      limits_by_type=dict(information=[min_information, max_information], summary=[min_summary, max_summary]),
+      strict_limits=strict_limits,
+      max_results=max_results,
+  )
+  return {'chatbot': rag_chatbot}
+
+@op("Chat processor")
+def chat_processor(processor, *, _ctx: one_by_one.Context):
+  cfg = _ctx.last_result or {'question_processors': [], 'answer_processors': [], 'masks': []}
+  for f in ['question_processor', 'answer_processor', 'mask']:
+    if f in processor:
+      cfg[f + 's'].append(processor[f])
+  question_processors = cfg['question_processors'][:]
+  answer_processors = cfg['answer_processors'][:]
+  masking_templates = {}
+  for mask in cfg['masks']:
+    masking_templates[mask['name']] = mask
+  if masking_templates:
+    question_processors.append(MaskTemplate(masking_templates=masking_templates))
+    answer_processors.append(MaskTemplate(masking_templates=masking_templates))
+  chat_processor = ChatProcessor(question_processors=question_processors, answer_processors=answer_processors)
+  return {'chat_processor': chat_processor, **cfg}
+
+@op("Truncate history")
+def truncate_history(*, max_tokens=10000, language='English'):
+  return {'question_processor': TruncateHistory(max_tokens=max_tokens, language=language.lower())}
+
+@op("Mask")
+def mask(*, name='', regex='', exceptions='', mask_pattern=''):
+  exceptions = [e.strip() for e in exceptions.split(',') if e.strip()]
+  return {'mask': {'name': name, 'regex': regex, 'exceptions': exceptions, 'mask_pattern': mask_pattern}}
+
+@ops.input_position(chat_api="bottom")
+@op("Test Chat API")
+def test_chat_api(message, chat_api):
+  chat_api = chat_api[0]['chat_api']
+  request = ChatAPIRequest(session_id="b43215a0-428f-11ef-9454-0242ac120002", question=message['text'], history=[])
+  response = asyncio.run(chat_api.answer(request))
+  return {'response': response.answer}
+
+@op("Input chat")
+def input_chat(*, chat: str):
+  return {'text': chat}
+
+@ops.input_position(chatbot="bottom", chat_processor="bottom")
+@op("Chat API")
+def chat_api(chatbot, chat_processor, knowledge_base, *, model='gpt-4o-mini'):
+  chatbot = chatbot[0]['chatbot']
+  chat_processor = chat_processor[0]['chat_processor']
+  c = ChatAPI(
+      chatbot=chatbot,
+      chat_processor=chat_processor,
+      model=model,
+  )
+  if knowledge_base:
+    c.chatbot.rag_graph.kg_base.load_v1_knowledge_base(**knowledge_base)
+    c.chatbot.scenario_selector.check_compatibility(c.chatbot.rag_graph)
+  return {'chat_api': c}
+
+@op("Knowledge base")
+def knowledge_base(*, nodes_path='nodes.pickle', edges_path='edges.pickle', template_cluster_path='tempclusters.pickle'):
+    return {'nodes_path': nodes_path, 'edges_path': edges_path, 'template_cluster_path': template_cluster_path}
+
+@op("View", view="table_view")
+def view(input):
+  columns = [str(c) for c in input.keys() if not str(c).startswith('_')]
+  v = {
+    'dataframes': { 'df': {
+      'columns': columns,
+      'data': [[input[c] for c in columns]],
+    }}
+  }
+  return v

web/package-lock.json

@@ -25,6 +25,7 @@
         "sass": "^1.77.2",
         "svelte": "^4.2.12",
         "svelte-check": "^3.6.9",
+        "svelte-markdown": "^0.4.1",
         "tslib": "^2.6.2",
         "typescript": "^5.4.4",
         "unplugin-icons": "^0.18.5",
@@ -1081,6 +1082,13 @@
       "resolved": "https://registry.npmjs.org/@types/geojson/-/geojson-7946.0.14.tgz",
       "integrity": "sha512-WCfD5Ht3ZesJUsONdhvm84dmzWOiOzOAqOncN0++w0lBw1o8OuDNJF2McvvCef/yBqb/HYRahp1BYtODFQ8bRg=="
     },
+    "node_modules/@types/marked": {
+      "version": "5.0.2",
+      "resolved": "https://registry.npmjs.org/@types/marked/-/marked-5.0.2.tgz",
+      "integrity": "sha512-OucS4KMHhFzhz27KxmWg7J+kIYqyqoW5kdIEI319hqARQQUTqhao3M/F+uFnDXD0Rg72iDDZxZNxq5gvctmLlg==",
+      "dev": true,
+      "license": "MIT"
+    },
     "node_modules/@types/pug": {
       "version": "2.0.10",
       "resolved": "https://registry.npmjs.org/@types/pug/-/pug-2.0.10.tgz",
@@ -1844,6 +1852,19 @@
         "@jridgewell/sourcemap-codec": "^1.4.15"
       }
     },
+    "node_modules/marked": {
+      "version": "5.1.2",
+      "resolved": "https://registry.npmjs.org/marked/-/marked-5.1.2.tgz",
+      "integrity": "sha512-ahRPGXJpjMjwSOlBoTMZAK7ATXkli5qCPxZ21TG44rx1KEo44bii4ekgTDQPNRQ4Kh7JMb9Ub1PVk1NxRSsorg==",
+      "dev": true,
+      "license": "MIT",
+      "bin": {
+        "marked": "bin/marked.js"
+      },
+      "engines": {
+        "node": ">= 16"
+      }
+    },
     "node_modules/mdn-data": {
       "version": "2.0.30",
       "resolved": "https://registry.npmjs.org/mdn-data/-/mdn-data-2.0.30.tgz",
@@ -2460,6 +2481,20 @@
         "svelte": "^3.19.0 || ^4.0.0"
       }
     },
+    "node_modules/svelte-markdown": {
+      "version": "0.4.1",
+      "resolved": "https://registry.npmjs.org/svelte-markdown/-/svelte-markdown-0.4.1.tgz",
+      "integrity": "sha512-pOlLY6EruKJaWI9my/2bKX8PdTeP5CM0s4VMmwmC2prlOkjAf+AOmTM4wW/l19Y6WZ87YmP8+ZCJCCwBChWjYw==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@types/marked": "^5.0.1",
+        "marked": "^5.1.2"
+      },
+      "peerDependencies": {
+        "svelte": "^4.0.0"
+      }
+    },
     "node_modules/svelte-preprocess": {
       "version": "5.1.4",
       "resolved": "https://registry.npmjs.org/svelte-preprocess/-/svelte-preprocess-5.1.4.tgz",

web/package.json

@@ -15,6 +15,7 @@
     "sass": "^1.77.2",
     "svelte": "^4.2.12",
     "svelte-check": "^3.6.9",
+    "svelte-markdown": "^0.4.1",
     "tslib": "^2.6.2",
     "typescript": "^5.4.4",
     "unplugin-icons": "^0.18.5",