Change dimensions of VectorField, move logic from view to controler. …

…Zbudowalismy go

api/bot/admin.py

@@ -1,4 +1,2 @@
 from django.contrib import admin
-from .models import Bot
 
-admin.site.register(Bot)

api/bot/controllers.py

@@ -1,9 +1,28 @@
 from http import HTTPStatus
 
-from bot.models import Bot
+import requests
+from pgvector.django import L2Distance
+
 from bot.schemas import BotIn, BotOut
+from chunks.models import Chunk
 
 
 def query_llm_controller(payload: BotIn) -> tuple[HTTPStatus, BotOut]:
-    bot = Bot(**payload.dict())
-    return HTTPStatus.CREATED, bot
+    # TODO: payload na embeding  -> Vector -> Szukamy w bazie podobne -> dokument do payloada
+    embeddings_body = {
+        "input": payload.input
+    }
+    response = requests.post("http://192.168.0.3:9000/v1/embeddings/", json=embeddings_body)
+    input_embedding = response.json()['data'][0]['embedding']
+    similar_chunk = Chunk.objects.order_by(L2Distance('embedding', input_embedding))[0]
+    print(similar_chunk.text)
+    llm_body = {
+        "prompt": "\n\n### Instructions:\nOdpowiedz na pytanie "+ payload.input +"\n\nWiedząc że"+similar_chunk.text+"\n\n### Response:\n",
+        "stop": [
+            "\n",
+            "###"
+        ]
+    }
+    llm_response = requests.post("http://192.168.0.3:9000/v1/completions/", json=llm_body)
+    llm_response = llm_response.json()['choices'][0]['text']
+    return HTTPStatus.OK, BotOut(output=str(llm_response))

api/bot/schemas.py

@@ -2,8 +2,12 @@
 
 
 class BotIn(Schema):
-    text: str
+    input: str
 
 
 class BotOut(Schema):
-    text: str
+    output: str
+
+
+class BotError(Schema):
+    pass

api/bot/views.py

@@ -1,27 +1,14 @@
-import requests
-
 from http import HTTPStatus
+
 from django.http import HttpRequest
-from pgvector.django import L2Distance
+from ninja import Router
 
-from bot.schemas import BotIn, BotOut
 from bot.controllers import query_llm_controller
-from chunks.models import Chunk
-from ninja import Router
+from bot.schemas import BotIn, BotOut, BotError
 
 router = Router(tags=["Bot"])
 
 
-@router.post("/bot/", response={HTTPStatus.CREATED: BotOut})
+@router.post("/bot/", response={HTTPStatus.OK: BotOut})
 def query_llm(request: HttpRequest, payload: BotIn):
-    # TODO: payload na embeding  -> Vector -> Szukamy w bazie podobne -> dokument do payloada
-    response = requests.post("http://192.168.0.3:9000/v1/embeddings", data=payload.text)
-
-    if not response.ok:
-        return HTTPStatus.INTERNAL_SERVER_ERROR
-
-    print(response.content)
-
-   # Chunk.objects.order_by(L2Distance('embedding', response))
-    response = requests.post("0.0.0.0:9000/v1/completions", data=payload)
-    return query_llm_controller(response)
+    return query_llm_controller(payload)

api/chunks/controllers.py

@@ -16,7 +16,7 @@ def create_chunk_controller(payload: ChunkIn) -> tuple[HTTPStatus, ChunkOut]:
     chunk_data["document_idx"] = document_instance
 
     response = requests.post(
-        url="http://0.0.0.0:9000/v1/embeddings",
+        url="http://0.0.0.0:9000/v1/embeddings/",
         data={"input": payload.text})
 
     if response.ok:

api/chunks/migrations/0001_initial.py

@@ -1,15 +1,16 @@
-# Generated by Django 5.0.2 on 2024-03-29 09:07
+# Generated by Django 4.2.13 on 2024-06-02 10:10
 
+from django.db import migrations, models
 import django.db.models.deletion
 import pgvector.django
-from django.db import migrations, models
 
 
 class Migration(migrations.Migration):
+
     initial = True
 
     dependencies = [
-        ("documents", "0001_initial"),
+        ("documents", "0002_remove_document_embedding"),
     ]
 
     operations = [
@@ -26,7 +27,7 @@ class Migration(migrations.Migration):
                     ),
                 ),
                 ("text", models.CharField(max_length=100)),
-                ("embedding", pgvector.django.VectorField(dimensions=10)),
+                ("embedding", pgvector.django.VectorField(dimensions=4096)),
                 ("chunk_idx", models.IntegerField()),
                 ("start_char", models.IntegerField()),
                 ("end_char", models.IntegerField()),

api/chunks/models.py

@@ -5,7 +5,7 @@
 
 class Chunk(models.Model):
     text = models.CharField[str, str](max_length=100)
-    embedding = VectorField[list[float], VectorField](dimensions=10)
+    embedding = VectorField[list[float], VectorField](dimensions=4096)
     chunk_idx = models.IntegerField[int, int]()
     start_char = models.IntegerField[int, int]()
     end_char = models.IntegerField[int, int]()