Add docs splitter and cross encoder based re-ranker; (#5)

Fix the formatting;

app.py

@@ -1,3 +1,4 @@
+import json
 from enum import Enum
 from typing import List, Optional, Union
 
@@ -10,6 +11,10 @@
     OpenAIEmbeddingModel,
     SentenceTransformerEmbeddingModel,
 )
+from reranker import get_scores
+
+# from langchain.schema import Document
+from splitter import get_split_documents_using_token_based
 
 load_dotenv()
 
@@ -35,6 +40,22 @@ class RequestSchemaForEmbeddings(BaseModel):
     base_url: Optional[str] = None
 
 
+class RequestSchemaForTextSplitter(BaseModel):
+    """Request Schema"""
+
+    model: str
+    documents: str
+    chunk_size: int
+    chunk_overlap: int
+
+
+class RequestSchemaForReRankers(BaseModel):
+    """Request Schema"""
+
+    query: str
+    documents: List[str]
+
+
 @app.get("/")
 async def home():
     """Returns a message"""
@@ -70,3 +91,18 @@ def generate(em_model, texts):
     elif type_model == EmbeddingModelType.OPENAI:
         embedding_model = OpenAIEmbeddingModel(model=name_model)
         return generate(em_model=embedding_model, texts=texts)
+
+
+@app.post("/split_docs_based_on_tokens")
+async def get_split_docs(item: RequestSchemaForTextSplitter):
+    """Splits the documents using the model tokenization method"""
+    docs = json.loads(item.documents)
+    return get_split_documents_using_token_based(
+        model_name=item.model, documents=docs, chunk_size=item.chunk_size, chunk_overlap=item.chunk_overlap
+    )
+
+
+@app.post("/docs_reranking_scores")
+async def get_reranked_docs(item: RequestSchemaForReRankers):
+    """Get reranked documents"""
+    return get_scores(item.query, item.documents)

docker-compose.yml

@@ -3,6 +3,7 @@ services:
     build: .
     volumes:
       - embedding_models:/opt/models
+      - .:/code
     command: bash -c 'uvicorn app:app --host=0.0.0.0 --port=8000'
     ports:
       - "8000:8000"

reranker.py

@@ -0,0 +1,13 @@
+from typing import List
+
+import torch.nn.functional as F
+from sentence_transformers import CrossEncoder
+from torch import Tensor
+
+
+def get_scores(query: str, documents: List[str], model_name: str = "cross-encoder/ms-marco-MiniLM-L-2-v2"):
+    """Get the scores"""
+    model = CrossEncoder(model_name=model_name, max_length=512)
+    doc_tuple = [(query, doc) for doc in documents]
+    scores = model.predict(doc_tuple)
+    return F.softmax(Tensor(scores), dim=0).tolist()

splitter.py

@@ -0,0 +1,31 @@
+from typing import List
+
+from langchain.schema import Document
+from langchain.text_splitter import SentenceTransformersTokenTextSplitter
+
+
+def langchain_document_to_dict(doc: Document):
+    """
+    Converts langchain Document to dictionary
+    """
+    return {"page_content": doc.page_content, "metadata": doc.metadata}
+
+
+def dict_to_langchain_document(doc: dict):
+    """
+    Converts dictionary to Langchain docuemnt
+    """
+    return Document(page_content=doc["page_content"], metadata=doc["metadata"])
+
+
+def get_split_documents_using_token_based(model_name: str, documents: List[dict], chunk_size: int, chunk_overlap: int):
+    """
+    Splits documents into multiple chunks using Sentence Transformer
+    token based.
+    """
+    splitter = SentenceTransformersTokenTextSplitter(
+        chunk_overlap=chunk_overlap, model_name=model_name, tokens_per_chunk=chunk_size
+    )
+    langchain_docs = [dict_to_langchain_document(d) for d in documents]
+    splitted_docs = splitter.split_documents(documents=langchain_docs)
+    return [langchain_document_to_dict(d) for d in splitted_docs]