diff --git a/examples/pipelines/adaptive-rag/Dockerfile b/examples/pipelines/adaptive-rag/Dockerfile
index 8ff0ad7..2e2ca8a 100644
--- a/examples/pipelines/adaptive-rag/Dockerfile
+++ b/examples/pipelines/adaptive-rag/Dockerfile
@@ -3,9 +3,12 @@ FROM pathwaycom/pathway:latest
 WORKDIR /app
 
 RUN apt-get update \
-    && apt-get install -y python3-opencv \
+    && apt-get install -y python3-opencv tesseract-ocr-eng \
     && rm -rf /var/lib/apt/lists/* /var/cache/apt/archives/*
 
+COPY requirements.txt .
+RUN pip install -U --no-cache-dir -r requirements.txt
+
 COPY . .
 
 EXPOSE 8000
diff --git a/examples/pipelines/adaptive-rag/app.py b/examples/pipelines/adaptive-rag/app.py
index a35a1bf..68f224c 100644
--- a/examples/pipelines/adaptive-rag/app.py
+++ b/examples/pipelines/adaptive-rag/app.py
@@ -2,71 +2,43 @@
 
 import pathway as pw
 from dotenv import load_dotenv
-from pathway.udfs import DiskCache, ExponentialBackoffRetryStrategy
-from pathway.xpacks.llm import embedders, llms, parsers, splitters
-from pathway.xpacks.llm.question_answering import AdaptiveRAGQuestionAnswerer
-from pathway.xpacks.llm.vector_store import VectorStoreServer
+from pathway.xpacks.llm.question_answering import SummaryQuestionAnswerer
+from pathway.xpacks.llm.servers import QASummaryRestServer
+from pydantic import BaseModel, ConfigDict, InstanceOf
 
 # To use advanced features with Pathway Scale, get your free license key from
 # https://pathway.com/features and paste it below.
 # To use Pathway Community, comment out the line below.
 pw.set_license_key("demo-license-key-with-telemetry")
 
-load_dotenv()
-
 logging.basicConfig(
     level=logging.INFO,
     format="%(asctime)s %(name)s %(levelname)s %(message)s",
     datefmt="%Y-%m-%d %H:%M:%S",
 )
 
+load_dotenv()
 
-if __name__ == "__main__":
-    path = "./data"
-
-    my_folder = pw.io.fs.read(
-        path=path,
-        format="binary",
-        with_metadata=True,
-    )
-
-    sources = [
-        my_folder
-    ]  # define the inputs (local folders, google drive, sharepoint, ...)
-
-    DEFAULT_GPT_MODEL = "gpt-3.5-turbo"
-
-    chat = llms.OpenAIChat(
-        model=DEFAULT_GPT_MODEL,
-        retry_strategy=ExponentialBackoffRetryStrategy(max_retries=6),
-        cache_strategy=DiskCache(),
-        temperature=0.0,
-    )
 
-    app_host = "0.0.0.0"
-    app_port = 8000
+class App(BaseModel):
+    question_answerer: InstanceOf[SummaryQuestionAnswerer]
+    host: str = "0.0.0.0"
+    port: int = 8000
 
-    parser = parsers.ParseUnstructured()
-    text_splitter = splitters.TokenCountSplitter(max_tokens=400)
-    embedder = embedders.OpenAIEmbedder(cache_strategy=DiskCache())
+    with_cache: bool = True
+    terminate_on_error: bool = False
 
-    vector_server = VectorStoreServer(
-        *sources,
-        embedder=embedder,
-        splitter=text_splitter,
-        parser=parser,
-    )
+    def run(self) -> None:
+        server = QASummaryRestServer(self.host, self.port, self.question_answerer)
+        server.run(
+            with_cache=self.with_cache, terminate_on_error=self.terminate_on_error
+        )
 
-    app = AdaptiveRAGQuestionAnswerer(
-        llm=chat,
-        indexer=vector_server,
-        default_llm_name=DEFAULT_GPT_MODEL,
-        n_starting_documents=2,
-        factor=2,
-        max_iterations=4,
-        strict_prompt=True,
-    )
+    model_config = ConfigDict(extra="forbid")
 
-    app.build_server(host=app_host, port=app_port)
 
-    app.run_server(with_cache=True)
+if __name__ == "__main__":
+    with open("app.yaml") as f:
+        config = pw.load_yaml(f)
+    app = App(**config)
+    app.run()
diff --git a/examples/pipelines/adaptive-rag/app.yaml b/examples/pipelines/adaptive-rag/app.yaml
new file mode 100644
index 0000000..e62536d
--- /dev/null
+++ b/examples/pipelines/adaptive-rag/app.yaml
@@ -0,0 +1,72 @@
+$sources:
+  - !pw.io.fs.read
+    path: data
+    format: binary
+    with_metadata: true
+
+  # - !pw.xpacks.connectors.sharepoint.read 
+  #   url: $SHAREPOINT_URL
+  #   tenant: $SHAREPOINT_TENANT
+  #   client_id: $SHAREPOINT_CLIENT_ID
+  #   cert_path: sharepointcert.pem
+  #   thumbprint: $SHAREPOINT_THUMBPRINT
+  #   root_path: $SHAREPOINT_ROOT
+  #   with_metadata: true
+  #   refresh_interval: 30
+
+  # - !pw.io.gdrive.read
+  #   object_id: $DRIVE_ID
+  #   service_user_credentials_file: gdrive_indexer.json
+  #   name_pattern:
+  #     - "*.pdf"
+  #     - "*.pptx"
+  #   object_size_limit: null
+  #   with_metadata: true
+  #   refresh_interval: 30
+
+$llm: !pw.xpacks.llm.llms.OpenAIChat
+  model: "gpt-3.5-turbo"
+  retry_strategy: !pw.udfs.ExponentialBackoffRetryStrategy
+    max_retries: 6
+  cache_strategy: !pw.udfs.DiskCache
+  temperature: 0.05
+  capacity: 8
+
+$embedder: !pw.xpacks.llm.embedders.OpenAIEmbedder
+  model: "text-embedding-ada-002"
+  cache_strategy: !pw.udfs.DiskCache
+
+$splitter: !pw.xpacks.llm.splitters.TokenCountSplitter
+  max_tokens: 400
+
+$parser: !pw.xpacks.llm.parsers.ParseUnstructured
+
+$retriever_factory: !pw.stdlib.indexing.BruteForceKnnFactory
+  reserved_space: 1000
+  embedder: $embedder
+  metric: !pw.internals.yaml_loader.import_object
+    path: pw.stdlib.indexing.BruteForceKnnMetricKind.COS
+  dimensions: 1536
+  
+
+$document_store: !pw.xpacks.llm.document_store.DocumentStore
+  docs: $sources
+  parser: $parser
+  splitter: $splitter
+  retriever_factory: $retriever_factory
+
+question_answerer: !pw.xpacks.llm.question_answering.AdaptiveRAGQuestionAnswerer
+  llm: $llm
+  indexer: $document_store
+  n_starting_documents: 2
+  factor: 2
+  max_iterations: 4
+  strict_prompt: true
+
+
+# Change host and port by uncommenting these files
+# host: "0.0.0.0"
+# port: 8000
+
+# with_cache: true
+# terminate_on_error: false
diff --git a/examples/pipelines/adaptive-rag/requirements.txt b/examples/pipelines/adaptive-rag/requirements.txt
new file mode 100644
index 0000000..3324f44
--- /dev/null
+++ b/examples/pipelines/adaptive-rag/requirements.txt
@@ -0,0 +1 @@
+python-dotenv==1.0.1
diff --git a/examples/pipelines/demo-document-indexing/.env b/examples/pipelines/demo-document-indexing/.env
deleted file mode 100644
index 69269f2..0000000
--- a/examples/pipelines/demo-document-indexing/.env
+++ /dev/null
@@ -1 +0,0 @@
-OPENAI_API_KEY=sk-*******
diff --git a/examples/pipelines/demo-document-indexing/Dockerfile b/examples/pipelines/demo-document-indexing/Dockerfile
index b5b23a9..2e2ca8a 100644
--- a/examples/pipelines/demo-document-indexing/Dockerfile
+++ b/examples/pipelines/demo-document-indexing/Dockerfile
@@ -3,15 +3,14 @@ FROM pathwaycom/pathway:latest
 WORKDIR /app
 
 RUN apt-get update \
-    && apt-get install -y python3-opencv \
+    && apt-get install -y python3-opencv tesseract-ocr-eng \
     && rm -rf /var/lib/apt/lists/* /var/cache/apt/archives/*
 
 COPY requirements.txt .
-
-RUN pip install --pre -U --no-cache-dir -r requirements.txt
+RUN pip install -U --no-cache-dir -r requirements.txt
 
 COPY . .
 
 EXPOSE 8000
 
-CMD ["python", "main.py"]
+CMD ["python", "app.py"]
diff --git a/examples/pipelines/demo-document-indexing/app.py b/examples/pipelines/demo-document-indexing/app.py
new file mode 100644
index 0000000..cee2cfb
--- /dev/null
+++ b/examples/pipelines/demo-document-indexing/app.py
@@ -0,0 +1,44 @@
+import logging
+
+import pathway as pw
+from dotenv import load_dotenv
+from pathway.xpacks.llm.document_store import DocumentStore
+from pathway.xpacks.llm.servers import DocumentStoreServer
+from pydantic import BaseModel, ConfigDict, InstanceOf
+
+# To use advanced features with Pathway Scale, get your free license key from
+# https://pathway.com/features and paste it below.
+# To use Pathway Community, comment out the line below.
+pw.set_license_key("demo-license-key-with-telemetry")
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s %(name)s %(levelname)s %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+)
+
+load_dotenv()
+
+
+class App(BaseModel):
+    document_store: InstanceOf[DocumentStore]
+    host: str = "0.0.0.0"
+    port: int = 8000
+
+    with_cache: bool = True
+    terminate_on_error: bool = False
+
+    def run(self) -> None:
+        server = DocumentStoreServer(self.host, self.port, self.document_store)
+        server.run(
+            with_cache=self.with_cache, terminate_on_error=self.terminate_on_error
+        )
+
+    model_config = ConfigDict(extra="forbid")
+
+
+if __name__ == "__main__":
+    with open("app.yaml") as f:
+        config = pw.load_yaml(f)
+    app = App(**config)
+    app.run()
diff --git a/examples/pipelines/demo-document-indexing/app.yaml b/examples/pipelines/demo-document-indexing/app.yaml
new file mode 100644
index 0000000..4f0f696
--- /dev/null
+++ b/examples/pipelines/demo-document-indexing/app.yaml
@@ -0,0 +1,67 @@
+$sources:
+  - !pw.io.fs.read
+    path: files-for-indexing
+    format: binary
+    with_metadata: true
+
+  # - !pw.xpacks.connectors.sharepoint.read 
+  #   url: $SHAREPOINT_URL
+  #   tenant: $SHAREPOINT_TENANT
+  #   client_id: $SHAREPOINT_CLIENT_ID
+  #   cert_path: sharepointcert.pem
+  #   thumbprint: $SHAREPOINT_THUMBPRINT
+  #   root_path: $SHAREPOINT_ROOT
+  #   with_metadata: true
+  #   refresh_interval: 30
+
+  # - !pw.io.gdrive.read
+  #   object_id: $DRIVE_ID
+  #   service_user_credentials_file: gdrive_indexer.json
+  #   name_pattern:
+  #     - "*.pdf"
+  #     - "*.pptx"
+  #   object_size_limit: null
+  #   with_metadata: true
+  #   refresh_interval: 30
+
+$llm: !pw.xpacks.llm.llms.OpenAIChat
+  model: "gpt-3.5-turbo"
+  retry_strategy: !pw.udfs.ExponentialBackoffRetryStrategy
+    max_retries: 6
+  cache_strategy: !pw.udfs.DiskCache
+  temperature: 0.05
+  capacity: 8
+
+$embedding_model: "mixedbread-ai/mxbai-embed-large-v1"
+
+$embedder: !pw.xpacks.llm.embedders.SentenceTransformerEmbedder
+  model: $embedding_model
+  call_kwargs: 
+    show_progress_bar: False
+
+$splitter: !pw.xpacks.llm.splitters.TokenCountSplitter
+  max_tokens: 400
+
+$parser: !pw.xpacks.llm.parsers.ParseUnstructured
+
+$retriever_factory: !pw.stdlib.indexing.BruteForceKnnFactory
+  reserved_space: 1000
+  embedder: $embedder
+  metric: !pw.internals.yaml_loader.import_object
+    path: pw.stdlib.indexing.BruteForceKnnMetricKind.COS
+  dimensions: 1536
+  
+
+document_store: !pw.xpacks.llm.document_store.DocumentStore
+  docs: $sources
+  parser: $parser
+  splitter: $splitter
+  retriever_factory: $retriever_factory
+
+
+# Change host and port by uncommenting these files
+# host: "0.0.0.0"
+# port: 8000
+
+# with_cache: true
+# terminate_on_error: false
diff --git a/examples/pipelines/demo-document-indexing/docker-compose.yml b/examples/pipelines/demo-document-indexing/docker-compose.yml
index b0bfd32..3a44dca 100644
--- a/examples/pipelines/demo-document-indexing/docker-compose.yml
+++ b/examples/pipelines/demo-document-indexing/docker-compose.yml
@@ -4,7 +4,7 @@ services:
     build:
       context: .
     ports:
-      - "8001:8000"
+      - "8000:8000"
     environment:
       OPENAI_API_KEY: "${OPENAI_API_KEY}"
     volumes:
diff --git a/examples/pipelines/demo-document-indexing/main.py b/examples/pipelines/demo-document-indexing/main.py
deleted file mode 100644
index 9b33eb9..0000000
--- a/examples/pipelines/demo-document-indexing/main.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import argparse
-import os
-import sys
-
-import pathway as pw
-import pathway.io.fs as io_fs
-import pathway.io.gdrive as io_gdrive
-import yaml
-from dotenv import load_dotenv
-from pathway.xpacks.llm import embedders, parsers, splitters, vector_store
-
-# To use advanced features with Pathway Scale, get your free license key from
-# https://pathway.com/features and paste it below.
-# To use Pathway Community, comment out the line below.
-pw.set_license_key("demo-license-key-with-telemetry")
-
-
-def data_sources(source_configs):
-    sources = []
-    for source_config in source_configs:
-        if source_config["kind"] == "local":
-            source = io_fs.read(
-                **source_config["config"],
-                format="binary",
-                with_metadata=True,
-            )
-            sources.append(source)
-        elif source_config["kind"] == "gdrive":
-            source = io_gdrive.read(
-                **source_config["config"],
-                with_metadata=True,
-            )
-            sources.append(source)
-        elif source_config["kind"] == "sharepoint":
-            try:
-                import pathway.xpacks.connectors.sharepoint as io_sp
-
-                source = io_sp.read(**source_config["config"], with_metadata=True)
-                sources.append(source)
-            except ImportError:
-                print(
-                    "The Pathway Sharepoint connector is part of the commercial offering, "
-                    "please contact us for a commercial license."
-                )
-                sys.exit(1)
-
-    return sources
-
-
-if __name__ == "__main__":
-    load_dotenv()
-    argparser = argparse.ArgumentParser(
-        prog="Pathway realtime document indexing demo",
-        description="""
-        This is the demo of real-time indexing of the documents from various data sources.
-        It runs a simple web server that is capable of answering queries on the endpoints
-        /v1/retrieve, /v1/statistics, /v1/inputs. Please refer to the "Test the REST endpoint"
-        section at Hosted Pipelines website: https://cloud.pathway.com.
-
-        Currently, it supports several data sources: the local one, Google Drive, and,
-        in a commercial offering, Microsoft SharePoint.
-
-        For the demo, you need to store your Open AI key in the OPENAI_API_KEY environment variable,
-        the easiest way is to add it to the .env file.
-        """,
-    )
-    argparser.add_argument(
-        "--host",
-        help="Host that will be used for running the web server.",
-        default="0.0.0.0",
-    )
-    argparser.add_argument(
-        "--port",
-        help="Port that will be used by the web server.",
-        type=int,
-        default=8000,
-    )
-    argparser.add_argument(
-        "--sources-config",
-        help="Path to sources configuration file",
-        default="sources_configuration.yaml",
-    )
-    args = argparser.parse_args()
-    with open(args.sources_config) as config_f:
-        configuration = yaml.safe_load(config_f)
-
-    openai_api_key = os.environ.get("OPENAI_API_KEY")
-    if openai_api_key is None:
-        print(
-            "Please set OPENAI_API_KEY either as a configuration variable, or in the .env file"
-        )
-        sys.exit(1)
-
-    splitter = splitters.TokenCountSplitter(max_tokens=200)
-    embedder = embedders.OpenAIEmbedder(api_key=openai_api_key)
-    parser = parsers.ParseUnstructured()
-    vs_server = vector_store.VectorStoreServer(
-        *data_sources(configuration["sources"]),
-        embedder=embedder,
-        splitter=splitter,
-        parser=parser,
-    )
-    vs_server.run_server(
-        host=args.host,
-        port=args.port,
-        threaded=False,
-        with_cache=True,
-    )
diff --git a/examples/pipelines/demo-document-indexing/requirements.txt b/examples/pipelines/demo-document-indexing/requirements.txt
index fb30529..3324f44 100644
--- a/examples/pipelines/demo-document-indexing/requirements.txt
+++ b/examples/pipelines/demo-document-indexing/requirements.txt
@@ -1,2 +1 @@
 python-dotenv==1.0.1
-mpmath==1.3.0
diff --git a/examples/pipelines/demo-document-indexing/sources_configuration.yaml b/examples/pipelines/demo-document-indexing/sources_configuration.yaml
deleted file mode 100644
index 9ad5742..0000000
--- a/examples/pipelines/demo-document-indexing/sources_configuration.yaml
+++ /dev/null
@@ -1,30 +0,0 @@
-sources:
-  - local_files:
-    kind: local
-    config:
-      # Please refer to
-      # https://pathway.com/developers/api-docs/pathway-io/fs#pathway.io.fs.read
-      # for options definition
-      path: "files-for-indexing/"
-  # - google_drive_folder:
-  #   kind: gdrive
-  #   config:
-  #     # Please refer to
-  #     # https://pathway.com/developers/api-docs/pathway-io/gdrive#pathway.io.gdrive.read
-  #     # for options definition
-  #     # Please follow https://pathway.com/developers/user-guide/connectors/gdrive-connector/#setting-up-google-drive
-  #     # for instructions on getting credentials
-  #     object_id: "1cULDv2OaViJBmOfG5WB0oWcgayNrGtVs" # folder used in the managed demo
-  #     service_user_credentials_file: SERVICE_CREDENTIALS
-  #     refresh_interval: 5
-  # - sharepoint_folder:
-  #   kind: sharepoint
-  #   config:
-  #     # The sharepoint is part of our commercial offering, please contact us to use it
-  #     root_path: ROOT_PATH
-  #     url: SHAREPOINT_URL
-  #     tenant: SHAREPOINT_TENANT
-  #     client_id: SHAREPOINT_CLIENT_ID
-  #     cert_path: SHAREPOINT.pem
-  #     thumbprint: SHAREPOINT_THUMBPRINT
-  #     refresh_interval: 5
diff --git a/examples/pipelines/demo-question-answering/Dockerfile b/examples/pipelines/demo-question-answering/Dockerfile
index a168de5..2e2ca8a 100644
--- a/examples/pipelines/demo-question-answering/Dockerfile
+++ b/examples/pipelines/demo-question-answering/Dockerfile
@@ -3,7 +3,7 @@ FROM pathwaycom/pathway:latest
 WORKDIR /app
 
 RUN apt-get update \
-    && apt-get install -y python3-opencv \
+    && apt-get install -y python3-opencv tesseract-ocr-eng \
     && rm -rf /var/lib/apt/lists/* /var/cache/apt/archives/*
 
 COPY requirements.txt .
diff --git a/examples/pipelines/demo-question-answering/README.md b/examples/pipelines/demo-question-answering/README.md
index 21b65a4..e8a8637 100644
--- a/examples/pipelines/demo-question-answering/README.md
+++ b/examples/pipelines/demo-question-answering/README.md
@@ -281,7 +281,7 @@ curl -X 'POST' \
   -H 'accept: */*' \
   -H 'Content-Type: application/json' \
   -d '{
-  "query": "string",
+  "query": "What is the start date of the contract?",
   "k": 2
 }'
 ```
diff --git a/examples/pipelines/demo-question-answering/app.py b/examples/pipelines/demo-question-answering/app.py
index 12108ac..68f224c 100644
--- a/examples/pipelines/demo-question-answering/app.py
+++ b/examples/pipelines/demo-question-answering/app.py
@@ -2,11 +2,9 @@
 
 import pathway as pw
 from dotenv import load_dotenv
-from pathway.xpacks import llm
-from pathway.xpacks.llm.question_answering import BaseRAGQuestionAnswerer
-from pathway.xpacks.llm.vector_store import VectorStoreServer
+from pathway.xpacks.llm.question_answering import SummaryQuestionAnswerer
+from pathway.xpacks.llm.servers import QASummaryRestServer
 from pydantic import BaseModel, ConfigDict, InstanceOf
-from typing_extensions import TypedDict
 
 # To use advanced features with Pathway Scale, get your free license key from
 # https://pathway.com/features and paste it below.
@@ -21,41 +19,26 @@
 
 load_dotenv()
 
-host_config = TypedDict("host_config", {"host": str, "port": int})
-
 
 class App(BaseModel):
-    llm: InstanceOf[pw.UDF]
-    embedder: InstanceOf[llm.embedders.BaseEmbedder]
-    splitter: InstanceOf[pw.UDF]
-    parser: InstanceOf[pw.UDF]
-
-    sources: list[InstanceOf[pw.Table]]
-
-    host_config: host_config
+    question_answerer: InstanceOf[SummaryQuestionAnswerer]
+    host: str = "0.0.0.0"
+    port: int = 8000
 
-    def run(self, config_file: str = "config.yaml") -> None:
-        # Unpack host and port from config
-        host, port = self.host_config["host"], self.host_config["port"]
+    with_cache: bool = True
+    terminate_on_error: bool = False
 
-        doc_store = VectorStoreServer(
-            *self.sources,
-            embedder=self.embedder,
-            splitter=self.splitter,
-            parser=self.parser,
+    def run(self) -> None:
+        server = QASummaryRestServer(self.host, self.port, self.question_answerer)
+        server.run(
+            with_cache=self.with_cache, terminate_on_error=self.terminate_on_error
         )
 
-        rag_app = BaseRAGQuestionAnswerer(llm=self.llm, indexer=doc_store)
-
-        rag_app.build_server(host=host, port=port)
-
-        rag_app.run_server(with_cache=True, terminate_on_error=False)
-
     model_config = ConfigDict(extra="forbid")
 
 
 if __name__ == "__main__":
-    with open("config.yaml") as f:
+    with open("app.yaml") as f:
         config = pw.load_yaml(f)
     app = App(**config)
     app.run()
diff --git a/examples/pipelines/demo-question-answering/app.yaml b/examples/pipelines/demo-question-answering/app.yaml
new file mode 100644
index 0000000..09458da
--- /dev/null
+++ b/examples/pipelines/demo-question-answering/app.yaml
@@ -0,0 +1,68 @@
+$sources:
+  - !pw.io.fs.read
+    path: data
+    format: binary
+    with_metadata: true
+
+  # - !pw.xpacks.connectors.sharepoint.read 
+  #   url: $SHAREPOINT_URL
+  #   tenant: $SHAREPOINT_TENANT
+  #   client_id: $SHAREPOINT_CLIENT_ID
+  #   cert_path: sharepointcert.pem
+  #   thumbprint: $SHAREPOINT_THUMBPRINT
+  #   root_path: $SHAREPOINT_ROOT
+  #   with_metadata: true
+  #   refresh_interval: 30
+
+  # - !pw.io.gdrive.read
+  #   object_id: $DRIVE_ID
+  #   service_user_credentials_file: gdrive_indexer.json
+  #   name_pattern:
+  #     - "*.pdf"
+  #     - "*.pptx"
+  #   object_size_limit: null
+  #   with_metadata: true
+  #   refresh_interval: 30
+
+$llm: !pw.xpacks.llm.llms.OpenAIChat
+  model: "gpt-3.5-turbo"
+  retry_strategy: !pw.udfs.ExponentialBackoffRetryStrategy
+    max_retries: 6
+  cache_strategy: !pw.udfs.DiskCache
+  temperature: 0.05
+  capacity: 8
+
+$embedder: !pw.xpacks.llm.embedders.OpenAIEmbedder
+  model: "text-embedding-ada-002"
+  cache_strategy: !pw.udfs.DiskCache
+
+$splitter: !pw.xpacks.llm.splitters.TokenCountSplitter
+  max_tokens: 400
+
+$parser: !pw.xpacks.llm.parsers.ParseUnstructured
+
+$retriever_factory: !pw.stdlib.indexing.BruteForceKnnFactory
+  reserved_space: 1000
+  embedder: $embedder
+  metric: !pw.internals.yaml_loader.import_object
+    path: pw.stdlib.indexing.BruteForceKnnMetricKind.COS
+  dimensions: 1536
+  
+
+$document_store: !pw.xpacks.llm.document_store.DocumentStore
+  docs: $sources
+  parser: $parser
+  splitter: $splitter
+  retriever_factory: $retriever_factory
+
+question_answerer: !pw.xpacks.llm.question_answering.BaseRAGQuestionAnswerer
+  llm: $llm
+  indexer: $document_store
+
+
+# Change host and port by uncommenting these files
+# host: "0.0.0.0"
+# port: 8000
+
+# with_cache: true
+# terminate_on_error: false
diff --git a/examples/pipelines/gpt_4o_multimodal_rag/README.md b/examples/pipelines/gpt_4o_multimodal_rag/README.md
index 31f04f3..2061fcb 100644
--- a/examples/pipelines/gpt_4o_multimodal_rag/README.md
+++ b/examples/pipelines/gpt_4o_multimodal_rag/README.md
@@ -162,17 +162,16 @@ curl -X 'POST'   'http://0.0.0.0:8000/v1/pw_ai_answer'   -H 'accept: */*'   -H '
 
 Looking good!
 
-## Modifying the code
+## Modifying the pipeline
 
-In the main function of `app.py`, we define:
-- input folders
+This template is easily configurable in the `app.yaml` file. In there you can define:
+- input sources
 - LLM
 - embedder
 - index
 - host and port to run the app
-- run options (caching, cache folder)
 
-You can modify any of the components by checking the options from the imported modules: `from pathway.xpacks.llm import embedders, llms, parsers, splitters`.
+You can modify any of the components by checking the options from the [Pathway LLM xpack](https://pathway.com/developers/api-docs/pathway-xpacks-llm).
 
 It is also possible to easily create new components by extending the [`pw.UDF`](https://pathway.com/developers/user-guide/data-transformation/user-defined-functions) class and implementing the `__wrapped__` function.
 
diff --git a/examples/pipelines/gpt_4o_multimodal_rag/app.py b/examples/pipelines/gpt_4o_multimodal_rag/app.py
index 6731d40..623c38a 100644
--- a/examples/pipelines/gpt_4o_multimodal_rag/app.py
+++ b/examples/pipelines/gpt_4o_multimodal_rag/app.py
@@ -10,113 +10,45 @@
         os.environ["TESSDATA_PREFIX"] = tesseract_dir  # fix for tesseract ocr
         break
 
-import sys
-
-import click
 import pathway as pw
-import pathway.io.fs as io_fs
-import pathway.io.gdrive as io_gdrive
-import yaml
 from dotenv import load_dotenv
-from pathway.udfs import DiskCache, ExponentialBackoffRetryStrategy
-from pathway.xpacks.llm import embedders, llms, prompts  # , parsers, splitters
-from pathway.xpacks.llm.parsers import OpenParse
-from pathway.xpacks.llm.question_answering import BaseRAGQuestionAnswerer
-from pathway.xpacks.llm.vector_store import VectorStoreServer
+from pathway.xpacks.llm.question_answering import SummaryQuestionAnswerer
+from pathway.xpacks.llm.servers import QASummaryRestServer
+from pydantic import BaseModel, ConfigDict, InstanceOf
 
 # To use advanced features with Pathway Scale, get your free license key from
 # https://pathway.com/features and paste it below.
 # To use Pathway Community, comment out the line below.
 pw.set_license_key("demo-license-key-with-telemetry")
 
-load_dotenv()
-
 logging.basicConfig(
     level=logging.INFO,
     format="%(asctime)s %(name)s %(levelname)s %(message)s",
     datefmt="%Y-%m-%d %H:%M:%S",
 )
 
+load_dotenv()
 
-def data_sources(source_configs) -> list[pw.Table]:
-    sources = []
-    for source_config in source_configs:
-        if source_config["kind"] == "local":
-            source = pw.io.fs.read(
-                **source_config["config"],
-                format="binary",
-                with_metadata=True,
-            )
-            sources.append(source)
-        elif source_config["kind"] == "gdrive":
-            source = pw.io.gdrive.read(
-                **source_config["config"],
-                with_metadata=True,
-            )
-            sources.append(source)
-        elif source_config["kind"] == "sharepoint":
-            try:
-                import pathway.xpacks.connectors.sharepoint as io_sp
-
-                source = io_sp.read(**source_config["config"], with_metadata=True)
-                sources.append(source)
-            except ImportError:
-                print(
-                    "The Pathway Sharepoint connector is part of the commercial offering, "
-                    "please contact us for a commercial license."
-                )
-                sys.exit(1)
-
-    return sources
 
+class App(BaseModel):
+    question_answerer: InstanceOf[SummaryQuestionAnswerer]
+    host: str = "0.0.0.0"
+    port: int = 8000
 
-@click.command()
-@click.option("--config_file", default="config.yaml", help="Config file to be used.")
-def run(
-    config_file: str = "config.yaml",
-):
-    with open(config_file) as config_f:
-        configuration = yaml.safe_load(config_f)
+    with_cache: bool = True
+    terminate_on_error: bool = False
 
-    openai_api_key = os.environ.get("OPENAI_API_KEY")
-    if openai_api_key is None:
-        print(
-            "Please set OPENAI_API_KEY either as a configuration variable, or in the .env file"
+    def run(self) -> None:
+        server = QASummaryRestServer(self.host, self.port, self.question_answerer)
+        server.run(
+            with_cache=self.with_cache, terminate_on_error=self.terminate_on_error
         )
-        sys.exit(1)
-
-    sources = data_sources(configuration["sources"])
-
-    chat = llms.OpenAIChat(
-        model="gpt-4o",
-        retry_strategy=ExponentialBackoffRetryStrategy(max_retries=6),
-        cache_strategy=DiskCache(),
-        temperature=0.0,
-    )
-
-    parser = OpenParse()
-    embedder = embedders.OpenAIEmbedder(cache_strategy=DiskCache())
-
-    doc_store = VectorStoreServer(
-        *sources,
-        embedder=embedder,
-        splitter=None,  # OpenParse parser handles the chunking
-        parser=parser,
-    )
-
-    app = BaseRAGQuestionAnswerer(
-        llm=chat,
-        indexer=doc_store,
-        search_topk=6,
-        short_prompt_template=prompts.prompt_qa,
-    )
-
-    host_config = configuration["host_config"]
-    host, port = host_config["host"], host_config["port"]
-    app.build_server(host=host, port=port)
 
-    app.run_server(with_cache=True, terminate_on_error=False)
+    model_config = ConfigDict(extra="forbid")
 
 
 if __name__ == "__main__":
-    run()
+    with open("app.yaml") as f:
+        config = pw.load_yaml(f)
+    app = App(**config)
+    app.run()
diff --git a/examples/pipelines/demo-question-answering/config.yaml b/examples/pipelines/gpt_4o_multimodal_rag/app.yaml
similarity index 54%
rename from examples/pipelines/demo-question-answering/config.yaml
rename to examples/pipelines/gpt_4o_multimodal_rag/app.yaml
index ddc010d..42bed4a 100644
--- a/examples/pipelines/demo-question-answering/config.yaml
+++ b/examples/pipelines/gpt_4o_multimodal_rag/app.yaml
@@ -1,25 +1,4 @@
-llm: !pw.xpacks.llm.llms.OpenAIChat
-  model: "gpt-3.5-turbo"
-  retry_strategy: !pw.udfs.ExponentialBackoffRetryStrategy
-    max_retries: 6
-  cache_strategy: !pw.udfs.DiskCache
-  temperature: 0.05
-  capacity: 8
-
-embedder: !pw.xpacks.llm.embedders.OpenAIEmbedder
-  model: "text-embedding-ada-002"
-  cache_strategy: !pw.udfs.DiskCache
-
-host_config:
-  host: "0.0.0.0"
-  port: 16003
-
-splitter: !pw.xpacks.llm.splitters.TokenCountSplitter
-  max_tokens: 400
-
-parser: !pw.xpacks.llm.parsers.ParseUnstructured
-
-sources:
+$sources:
   - !pw.io.fs.read
     path: data
     format: binary
@@ -44,3 +23,42 @@ sources:
   #   object_size_limit: null
   #   with_metadata: true
   #   refresh_interval: 30
+
+$llm: !pw.xpacks.llm.llms.OpenAIChat
+  model: "gpt-3.5-turbo"
+  retry_strategy: !pw.udfs.ExponentialBackoffRetryStrategy
+    max_retries: 6
+  cache_strategy: !pw.udfs.DiskCache
+  temperature: 0.05
+  capacity: 8
+
+$embedder: !pw.xpacks.llm.embedders.OpenAIEmbedder
+  model: "text-embedding-ada-002"
+  cache_strategy: !pw.udfs.DiskCache
+
+$parser: !pw.xpacks.llm.parsers.OpenParse
+  cache_strategy: !pw.udfs.DiskCache
+
+$retriever_factory: !pw.stdlib.indexing.BruteForceKnnFactory
+  reserved_space: 1000
+  embedder: $embedder
+  metric: !pw.engine.BruteForceKnnMetricKind.COS
+  dimensions: 1536
+  
+
+$document_store: !pw.xpacks.llm.document_store.DocumentStore
+  docs: $sources
+  parser: $parser
+  retriever_factory: $retriever_factory
+
+question_answerer: !pw.xpacks.llm.question_answering.BaseRAGQuestionAnswerer
+  llm: $llm
+  indexer: $document_store
+
+
+# Change host and port by uncommenting these files
+# host: "0.0.0.0"
+# port: 8000
+
+# with_cache: true
+# terminate_on_error: false
diff --git a/examples/pipelines/gpt_4o_multimodal_rag/config.yaml b/examples/pipelines/gpt_4o_multimodal_rag/config.yaml
deleted file mode 100644
index 31b5482..0000000
--- a/examples/pipelines/gpt_4o_multimodal_rag/config.yaml
+++ /dev/null
@@ -1,34 +0,0 @@
-host_config:
-  host: "0.0.0.0"
-  port: 8000
-sources:
-  - local_files:
-    kind: local
-    config:
-      # Please refer to
-      # https://pathway.com/developers/api-docs/pathway-io/fs#pathway.io.fs.read
-      # for options definition
-      path: "data/"
-  # - google_drive_folder:
-  #   kind: gdrive
-  #   config:
-  #     # Please refer to
-  #     # https://pathway.com/developers/api-docs/pathway-io/gdrive#pathway.io.gdrive.read
-  #     # for options definition
-  #     # Please follow https://pathway.com/developers/user-guide/connectors/gdrive-connector/#setting-up-google-drive
-  #     # for instructions on getting credentials
-  #     object_id: "1fUU13kooK2qFnGOO0FAO0a0RWmtZJhX-" # folder used in the managed demo
-  #     service_user_credentials_file: sources/gdrive-indexer.json
-  #     refresh_interval: 5
-  # - sharepoint_folder:
-  #   kind: sharepoint
-  #   config:
-  #     # The sharepoint is part of our commercial offering, please contact us to use it
-  #     root_path: ROOT_PATH
-  #     url: SHAREPOINT_URL
-  #     tenant: SHAREPOINT_TENANT
-  #     client_id: SHAREPOINT_CLIENT_ID
-  #     cert_path: SHAREPOINT.pem
-  #     thumbprint: SHAREPOINT_THUMBPRINT
-  #     refresh_interval: 5
-
diff --git a/examples/pipelines/private-rag/Dockerfile b/examples/pipelines/private-rag/Dockerfile
index 8ff0ad7..2e2ca8a 100644
--- a/examples/pipelines/private-rag/Dockerfile
+++ b/examples/pipelines/private-rag/Dockerfile
@@ -3,9 +3,12 @@ FROM pathwaycom/pathway:latest
 WORKDIR /app
 
 RUN apt-get update \
-    && apt-get install -y python3-opencv \
+    && apt-get install -y python3-opencv tesseract-ocr-eng \
     && rm -rf /var/lib/apt/lists/* /var/cache/apt/archives/*
 
+COPY requirements.txt .
+RUN pip install -U --no-cache-dir -r requirements.txt
+
 COPY . .
 
 EXPOSE 8000
diff --git a/examples/pipelines/private-rag/app.py b/examples/pipelines/private-rag/app.py
index 36b84a7..68f224c 100644
--- a/examples/pipelines/private-rag/app.py
+++ b/examples/pipelines/private-rag/app.py
@@ -1,79 +1,44 @@
 import logging
-import os
 
 import pathway as pw
 from dotenv import load_dotenv
-from pathway.xpacks.llm import embedders, llms, parsers, splitters
-from pathway.xpacks.llm.question_answering import AdaptiveRAGQuestionAnswerer
-from pathway.xpacks.llm.vector_store import VectorStoreServer
+from pathway.xpacks.llm.question_answering import SummaryQuestionAnswerer
+from pathway.xpacks.llm.servers import QASummaryRestServer
+from pydantic import BaseModel, ConfigDict, InstanceOf
 
 # To use advanced features with Pathway Scale, get your free license key from
 # https://pathway.com/features and paste it below.
 # To use Pathway Community, comment out the line below.
 pw.set_license_key("demo-license-key-with-telemetry")
 
-load_dotenv()
-
 logging.basicConfig(
     level=logging.INFO,
     format="%(asctime)s %(name)s %(levelname)s %(message)s",
     datefmt="%Y-%m-%d %H:%M:%S",
 )
 
+load_dotenv()
 
-if __name__ == "__main__":
-    path = "./data"
-
-    my_folder = pw.io.fs.read(
-        path=path,
-        format="binary",
-        with_metadata=True,
-    )
-
-    sources = [
-        my_folder
-    ]  # define the inputs (local folders, google drive, sharepoint, ...)
-
-    DEFAULT_MODEL = "ollama/mistral"
-    api_base = os.environ.get("LLM_API_BASE", "http://localhost:11434")
-
-    chat = llms.LiteLLMChat(
-        model=DEFAULT_MODEL,
-        temperature=0,
-        top_p=1,
-        api_base=api_base,  # local deployment
-        format="json",  # only available in Ollama local deploy, not usable in Mistral API
-    )
-
-    app_host = "0.0.0.0"
-    app_port = 8000
-
-    parser = parsers.ParseUnstructured()
-    text_splitter = splitters.TokenCountSplitter(max_tokens=400)
 
-    embedding_model = "avsolatorio/GIST-small-Embedding-v0"
+class App(BaseModel):
+    question_answerer: InstanceOf[SummaryQuestionAnswerer]
+    host: str = "0.0.0.0"
+    port: int = 8000
 
-    embedder = embedders.SentenceTransformerEmbedder(
-        embedding_model, call_kwargs={"show_progress_bar": False}
-    )
+    with_cache: bool = True
+    terminate_on_error: bool = False
 
-    vector_server = VectorStoreServer(
-        *sources,
-        embedder=embedder,
-        splitter=text_splitter,
-        parser=parser,
-    )
+    def run(self) -> None:
+        server = QASummaryRestServer(self.host, self.port, self.question_answerer)
+        server.run(
+            with_cache=self.with_cache, terminate_on_error=self.terminate_on_error
+        )
 
-    app = AdaptiveRAGQuestionAnswerer(
-        llm=chat,
-        indexer=vector_server,
-        default_llm_name=DEFAULT_MODEL,
-        n_starting_documents=2,
-        factor=2,
-        max_iterations=4,
-        strict_prompt=True,
-    )
+    model_config = ConfigDict(extra="forbid")
 
-    app.build_server(host=app_host, port=app_port)
 
-    app.run_server(with_cache=True)
+if __name__ == "__main__":
+    with open("app.yaml") as f:
+        config = pw.load_yaml(f)
+    app = App(**config)
+    app.run()
diff --git a/examples/pipelines/private-rag/app.yaml b/examples/pipelines/private-rag/app.yaml
new file mode 100644
index 0000000..f6aa160
--- /dev/null
+++ b/examples/pipelines/private-rag/app.yaml
@@ -0,0 +1,78 @@
+$sources:
+  - !pw.io.fs.read
+    path: data
+    format: binary
+    with_metadata: true
+
+  # - !pw.xpacks.connectors.sharepoint.read 
+  #   url: $SHAREPOINT_URL
+  #   tenant: $SHAREPOINT_TENANT
+  #   client_id: $SHAREPOINT_CLIENT_ID
+  #   cert_path: sharepointcert.pem
+  #   thumbprint: $SHAREPOINT_THUMBPRINT
+  #   root_path: $SHAREPOINT_ROOT
+  #   with_metadata: true
+  #   refresh_interval: 30
+
+  # - !pw.io.gdrive.read
+  #   object_id: $DRIVE_ID
+  #   service_user_credentials_file: gdrive_indexer.json
+  #   name_pattern:
+  #     - "*.pdf"
+  #     - "*.pptx"
+  #   object_size_limit: null
+  #   with_metadata: true
+  #   refresh_interval: 30
+
+$llm_model: "ollama/mistral"
+
+$llm: !pw.xpacks.llm.llms.LiteLLMChat
+  model: $llm_model
+  retry_strategy: !pw.udfs.ExponentialBackoffRetryStrategy
+    max_retries: 6
+  cache_strategy: !pw.udfs.DiskCache
+  temperature: 0
+  top_p: 1
+  format: "json"  # only available in Ollama local deploy, not usable in Mistral API
+  api_base: "http://localhost:11434"
+
+$embedding_model: "avsolatorio/GIST-small-Embedding-v0"
+
+$embedder: !pw.xpacks.llm.embedders.SentenceTransformerEmbedder
+  model: $embedding_model
+  call_kwargs: 
+    show_progress_bar: False
+
+$splitter: !pw.xpacks.llm.splitters.TokenCountSplitter
+  max_tokens: 400
+
+$parser: !pw.xpacks.llm.parsers.ParseUnstructured
+
+$retriever_factory: !pw.stdlib.indexing.BruteForceKnnFactory
+  reserved_space: 1000
+  embedder: $embedder
+  metric: !pw.engine.BruteForceKnnMetricKind.COS
+  dimensions: 1536
+  
+
+$document_store: !pw.xpacks.llm.document_store.DocumentStore
+  docs: $sources
+  parser: $parser
+  splitter: $splitter
+  retriever_factory: $retriever_factory
+
+question_answerer: !pw.xpacks.llm.question_answering.AdaptiveRAGQuestionAnswerer
+  llm: $llm
+  indexer: $document_store
+  n_starting_documents: 2
+  factor: 2
+  max_iterations: 4
+  strict_prompt: true
+
+
+# Change host and port by uncommenting these files
+# host: "0.0.0.0"
+# port: 8000
+
+# with_cache: true
+# terminate_on_error: false
diff --git a/examples/pipelines/private-rag/requirements.txt b/examples/pipelines/private-rag/requirements.txt
new file mode 100644
index 0000000..fb30529
--- /dev/null
+++ b/examples/pipelines/private-rag/requirements.txt
@@ -0,0 +1,2 @@
+python-dotenv==1.0.1
+mpmath==1.3.0
diff --git a/pyproject.toml b/pyproject.toml
index 7462c50..428d5fc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -81,7 +81,7 @@ profile = "black"
 
 [tool.mypy]
 python_version = "3.10"
-exclude = ["examples/data", "examples/pipelines/adaptive-rag", "examples/pipelines/private-rag"]
+exclude = ["examples/data", "examples/pipelines/adaptive-rag", "examples/pipelines/private-rag", "examples/pipelines/demo-document-indexing"]
 ignore_missing_imports = true
 check_untyped_defs = true
 warn_redundant_casts = true