chore: Add PyPI package support for uvx/pip installation (#1764)

* Initial plan

* Add package structure and resource path utilities

- Created langbot/ package with __init__.py and __main__.py entry point
- Added paths utility to find frontend and resource files from package installation
- Updated config loading to use resource paths
- Updated frontend serving to use resource paths
- Added MANIFEST.in for package data inclusion
- Updated pyproject.toml with build system and entry points

Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com>

* Add PyPI publishing workflow and update license

- Created GitHub Actions workflow to build frontend and publish to PyPI
- Added license field to pyproject.toml to fix deprecation warning
- Updated .gitignore to exclude build artifacts
- Tested package building successfully

Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com>

* Add PyPI installation documentation

- Created PYPI_INSTALLATION.md with detailed installation and usage instructions
- Updated README.md to feature uvx/pip installation as recommended method
- Updated README_EN.md with same changes for English documentation

Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com>

* Address code review feedback

- Made package-data configuration more specific to langbot package only
- Improved path detection with caching to avoid repeated file I/O
- Removed sys.path searching which was incorrect for package data
- Removed interactive input() call for non-interactive environment compatibility
- Simplified error messages for version check

Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com>

* Fix code review issues

- Use specific exception types instead of bare except
- Fix misleading comments about directory levels
- Remove redundant existence check before makedirs with exist_ok=True
- Use context manager for file opening to ensure proper cleanup

Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com>

* Simplify package configuration and document behavioral differences

- Removed redundant package-data configuration, relying on MANIFEST.in
- Added documentation about behavioral differences between package and source installation
- Clarified that include-package-data=true uses MANIFEST.in for data files

Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com>

* chore: update pyproject.toml

* chore: try pack templates in langbot/

* chore: update

* chore: update

* chore: update

* chore: update

* chore: update

* chore: adjust dir structure

* chore: fix imports

* fix: read default-pipeline-config.json

* fix: read default-pipeline-config.json

* fix: tests

* ci: publish pypi

* chore: bump version 4.6.0-beta.1 for testing

* chore: add templates/**

* fix: send adapters and requesters icons

* chore: bump version 4.6.0b2 for testing

* chore: add platform field for docker-compose.yaml

---------

Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com>
Co-authored-by: Junyan Qin <rockchinq@gmail.com>

src/langbot/pkg/rag/knowledge/services/retriever.py

@@ -0,0 +1,48 @@
+from __future__ import annotations
+
+from . import base_service
+from ....core import app
+from ....provider.modelmgr.requester import RuntimeEmbeddingModel
+from ....entity.rag import retriever as retriever_entities
+
+
+class Retriever(base_service.BaseService):
+    def __init__(self, ap: app.Application):
+        super().__init__()
+        self.ap = ap
+
+    async def retrieve(
+        self, kb_id: str, query: str, embedding_model: RuntimeEmbeddingModel, k: int = 5
+    ) -> list[retriever_entities.RetrieveResultEntry]:
+        self.ap.logger.info(
+            f"Retrieving for query: '{query[:10]}' with k={k} using {embedding_model.model_entity.uuid}"
+        )
+
+        query_embedding: list[float] = await embedding_model.requester.invoke_embedding(
+            model=embedding_model,
+            input_text=[query],
+            extra_args={},  # TODO: add extra args
+        )
+
+        vector_results = await self.ap.vector_db_mgr.vector_db.search(kb_id, query_embedding[0], k)
+
+        # 'ids' shape mirrors the Chroma-style response contract for compatibility
+        matched_vector_ids = vector_results.get('ids', [[]])[0]
+        distances = vector_results.get('distances', [[]])[0]
+        vector_metadatas = vector_results.get('metadatas', [[]])[0]
+
+        if not matched_vector_ids:
+            self.ap.logger.info('No relevant chunks found in vector database.')
+            return []
+
+        result: list[retriever_entities.RetrieveResultEntry] = []
+
+        for i, id in enumerate(matched_vector_ids):
+            entry = retriever_entities.RetrieveResultEntry(
+                id=id,
+                metadata=vector_metadatas[i],
+                distance=distances[i],
+            )
+            result.append(entry)
+
+        return result