langchain uploading new way of predefined paths from yandex disk

services/rag/llamaindex/PLANNING.md

@@ -67,5 +67,5 @@ Chosen data folder: relatve ./../../../data - from the current folder
 
 # Phase 11 (http endpoint to retrieve data from the vector storage by query)
 
-- [ ] Create file `server.py`, with web framework fastapi, for example
-- [ ] Add POST endpoint "/api/test-query" which will use agent, and retrieve response for query, sent in JSON format, field "query"
+- [x] Create file `server.py`, with web framework fastapi, for example
+- [x] Add POST endpoint "/api/test-query" which will use agent, and retrieve response for query, sent in JSON format, field "query"

services/rag/llamaindex/server.py

@@ -0,0 +1,109 @@
+#!/usr/bin/env python3
+"""
+HTTP API server for querying the vector storage via the existing retrieval pipeline.
+"""
+
+from pathlib import Path
+import sys
+
+from dotenv import load_dotenv
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel, Field
+from loguru import logger
+
+from retrieval import initialize_retriever
+
+load_dotenv()
+
+
+def setup_logging() -> None:
+    """Configure loguru to stdout and rotating file logs."""
+    logs_dir = Path("logs")
+    logs_dir.mkdir(exist_ok=True)
+
+    logger.remove()
+    logger.add(
+        "logs/dev.log",
+        rotation="10 MB",
+        retention="10 days",
+        level="INFO",
+        format="{time:YYYY-MM-DD HH:mm:ss} | {level} | {file}:{line} | {message}",
+    )
+    logger.add(
+        sys.stdout,
+        level="INFO",
+        format="{time:YYYY-MM-DD HH:mm:ss} | {level} | {message}",
+        colorize=True,
+    )
+
+
+setup_logging()
+
+app = FastAPI(title="LlamaIndex RAG API", version="1.0.0")
+
+
+class TestQueryRequest(BaseModel):
+    query: str = Field(..., min_length=1, description="User query text")
+    top_k: int = Field(5, ge=1, le=20, description="Number of retrieved chunks")
+
+
+class SourceItem(BaseModel):
+    content: str
+    score: float | None = None
+    metadata: dict = Field(default_factory=dict)
+
+
+class TestQueryResponse(BaseModel):
+    query: str
+    response: str
+    sources: list[SourceItem]
+
+
+@app.get("/health")
+def health() -> dict[str, str]:
+    return {"status": "ok"}
+
+
+@app.post("/api/test-query", response_model=TestQueryResponse)
+def test_query(payload: TestQueryRequest) -> TestQueryResponse:
+    """
+    Query the vector store using the existing retrieval/query engine.
+    """
+    query = payload.query.strip()
+    if not query:
+        raise HTTPException(status_code=400, detail="Field 'query' must not be empty")
+
+    logger.info(f"Received /api/test-query request (top_k={payload.top_k})")
+
+    try:
+        query_engine = initialize_retriever(similarity_top_k=payload.top_k)
+        result = query_engine.query(query)
+
+        sources: list[SourceItem] = []
+        if hasattr(result, "source_nodes"):
+            for node in result.source_nodes:
+                sources.append(
+                    SourceItem(
+                        content=str(getattr(node, "text", "")),
+                        score=getattr(node, "score", None),
+                        metadata=getattr(node, "metadata", {}) or {},
+                    )
+                )
+
+        response_text = str(result)
+        logger.info(
+            f"/api/test-query completed successfully (sources={len(sources)})"
+        )
+        return TestQueryResponse(query=query, response=response_text, sources=sources)
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"/api/test-query failed: {e}")
+        raise HTTPException(status_code=500, detail="Failed to process query")
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run("server:app", host="0.0.0.0", port=8000, reload=False)