Remove db

.env.example

@@ -1,5 +1,3 @@
-DATABASE_URL=
-DATABASE_MIGRATION_URL=
 OPENAI_API_KEY=
 HF_API_KEY=
 PINECONE_API_KEY=

.gitignore

@@ -7,4 +7,5 @@ superenv/
 .DS_Store
 venv/
 /.vscode
-/.codesandbox
+/.codesandbox
+.pypirc

lib/__init__.py

@@ -0,0 +1,3 @@
+# flake8: noqa
+
+from .service import create_finetuned_model, create_vector_embeddings

lib/service/__init__.py

@@ -0,0 +1,48 @@
+from typing import List, Union
+
+import openai
+from llama_index import Document
+
+from lib.service.embedding import EmbeddingService
+from lib.service.finetune import get_finetuning_service
+
+
+def create_vector_embeddings(
+    type: str, finetune_id: str, url: str = None, content: str = None
+) -> List[Union[Document, None]]:
+    embedding_service = EmbeddingService(type=type, content=content, url=url)
+    documents = embedding_service.generate_documents()
+    nodes = embedding_service.generate_chunks(documents=documents)
+    embedding_service.generate_embeddings(nodes=nodes, finetune_id=finetune_id)
+    return nodes
+
+
+def create_finetuned_model(
+    provider: str,
+    base_model: str,
+    type: str,
+    url: str = None,
+    content: str = None,
+    webhook_url: str = None,
+):
+    embedding_service = EmbeddingService(type=type, url=url, content=content)
+    documents = embedding_service.generate_documents()
+    nodes = embedding_service.generate_chunks(documents=documents)
+    finetunning_service = get_finetuning_service(
+        nodes=nodes,
+        provider=provider,
+        batch_size=5,
+        base_model=base_model,
+        num_questions_per_chunk=1,
+    )
+    training_file = finetunning_service.generate_dataset()
+    formatted_training_file = finetunning_service.validate_dataset(
+        training_file=training_file
+    )
+    finetune = finetunning_service.finetune(
+        training_file=formatted_training_file, webhook_url=webhook_url
+    )
+    if provider == "OPENAI":
+        finetune = openai.FineTune.retrieve(id=finetune.get("id"))
+    finetunning_service.cleanup(training_file=finetune.get("training_file"))
+    return finetune

lib/service/embedding.py

@@ -9,48 +9,49 @@
 from sentence_transformers import SentenceTransformer
 
 from lib.service.vectordb import get_vector_service
-from prisma.models import Datasource
 
 
 class EmbeddingService:
-    def __init__(self, datasource: Datasource):
-        self.datasource = datasource
+    def __init__(self, type: str, url: str = None, content: str = None):
+        self.type = type
+        self.url = url
+        self.content = content
 
     def get_datasource_suffix(self) -> str:
         suffixes = {"TXT": ".txt", "PDF": ".pdf", "MARKDOWN": ".md"}
         try:
-            return suffixes[self.datasource.type]
+            return suffixes[self.type]
         except KeyError:
             raise ValueError("Unsupported datasource type")
 
-    async def generate_documents(self) -> List[Document]:
+    def generate_documents(self) -> List[Document]:
         with NamedTemporaryFile(
             suffix=self.get_datasource_suffix(), delete=True
         ) as temp_file:
-            if self.datasource.url:
-                content = requests.get(self.datasource.url).content
+            if self.url:
+                content = requests.get(self.url).content
             else:
-                content = self.datasource.content
+                content = self.content
             temp_file.write(content)
             temp_file.flush()
             reader = SimpleDirectoryReader(input_files=[temp_file.name])
             docs = reader.load_data()
             return docs
 
-    async def generate_chunks(
-        self, documents: List[Document]
-    ) -> List[Union[Document, None]]:
+    def generate_chunks(self, documents: List[Document]) -> List[Union[Document, None]]:
         parser = SimpleNodeParser.from_defaults(chunk_size=350, chunk_overlap=20)
         nodes = parser.get_nodes_from_documents(documents, show_progress=True)
         return nodes
 
-    async def generate_embeddings(
-        self, nodes: List[Union[Document, None]]
+    def generate_embeddings(
+        self,
+        nodes: List[Union[Document, None]],
+        finetune_id: str,
     ) -> List[ndarray]:
-        vectordb = await get_vector_service(
+        vectordb = get_vector_service(
             provider="pinecone",
             index_name="all-minilm-l6-v2",
-            namespace=self.datasource.id,
+            namespace=finetune_id,
             dimension=384,
         )
         model = SentenceTransformer(
@@ -65,7 +66,7 @@ async def generate_embeddings(
                     {**node.metadata, "content": node.text},
                 )
                 embeddings.append(embedding)
-        await vectordb.upsert(vectors=embeddings)
+        vectordb.upsert(vectors=embeddings)
         return embeddings
 
     # def generate_query(self):