tst

.gitignore

@@ -11,5 +11,5 @@ __pycache__/
 venv/
 
 # Exclude lock and meta files
-*.lock
-meta.json
+# *.lock
+# meta.json

api.py

@@ -101,31 +101,30 @@ def search(Item:Item):
 async def ask_localai(item: Item):
     query = item.query
     
-    # search_result = qdrant.similarity_search(query=query, k=3)
-    return {"item": item, "search_result": query}
-    # if not search_result:
-    #     return {"error": "No relevant results found for the query."}
-
-    # context = " ".join([res.page_content for res in search_result])
-    # if not context.strip():
-    #     return {"error": "No relevant context found."}
-
-    # try:
-    #     prompt = (
-    #         f"Context: {context}\n\n"
-    #         f"Question: {query}\n"
-    #         f"Answer concisely and only based on the context provided. Do not repeat the context or the question.\n"
-    #         f"Answer:"
-    #     )
-    #     qa_result = qa_pipeline(question=query, context=context)
-    #     answer = qa_result["answer"]
-
-    #     return {
-    #         "question": query,
-    #         "answer": answer
-    #     }
-    # except Exception as e:
-    #     return {"error": "Failed to generate an answer."}
+    search_result = qdrant.similarity_search(query=query, k=3)
+    if not search_result:
+        return {"error": "No relevant results found for the query."}
+
+    context = " ".join([res.page_content for res in search_result])
+    if not context.strip():
+        return {"error": "No relevant context found."}
+
+    try:
+        prompt = (
+            f"Context: {context}\n\n"
+            f"Question: {query}\n"
+            f"Answer concisely and only based on the context provided. Do not repeat the context or the question.\n"
+            f"Answer:"
+        )
+        qa_result = qa_pipeline(question=query, context=context)
+        answer = qa_result["answer"]
+
+        return {
+            "question": query,
+            "answer": answer
+        }
+    except Exception as e:
+        return {"error": "Failed to generate an answer."}
 
 @app.get("/items/{item_id}")
 def read_item(item_id: int, q: str = None):

qdrant/.lock

@@ -0,0 +1 @@
+tmp lock file

qdrant/meta.json

@@ -0,0 +1 @@
+{"collections": {"MyCollection": {"vectors": {"size": 384, "distance": "Dot", "hnsw_config": null, "quantization_config": null, "on_disk": null, "datatype": null, "multivector_config": null}, "shard_number": null, "sharding_method": null, "replication_factor": null, "write_consistency_factor": null, "on_disk_payload": null, "hnsw_config": null, "wal_config": null, "optimizers_config": null, "init_from": null, "quantization_config": null, "sparse_vectors": null}}, "aliases": {}}