refactor to functions

Signed-off-by: greg pereira <[email protected]>

.gitignore

@@ -13,6 +13,7 @@ generated
 .idea
 .DS_Store
 milvus/seed/data/*
+milvus/build/volumes/milvus/*data*
 *.venv
 *venv
 

milvus/seed/seed.py

@@ -1,38 +1,76 @@
 import os
 from pymilvus import MilvusClient, DataType
+from langchain_community.vectorstores import Milvus
 from langchain_experimental.text_splitter import SemanticChunker
 from langchain_community.document_loaders import PyPDFLoader, WebBaseLoader
 from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceInstructEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain import hub
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.output_parsers import StrOutputParser
 from tika import parser # pip install tika
 
+
 def log_step(step_num, step_name) -> None:
     print("-----------------------------------------------")
     print(f"{step_num}. {step_name}")
     print("-----------------------------------------------")
 
-# model_name = "ibm/merlinite-7b"
-# model_kwargs = {"device": "cpu"}
-# encode_kwargs = {"normalize_embeddings": True}
+def milvus_init() -> MilvusClient:
+    client = MilvusClient()
+    if not client.has_connection('dnd'):
+        client.drop_connection('dnd')
+    return client
 
-model_name = "ibm/merlinite-7b"
-model_kwargs={"device": "cuda"},
-encode_kwargs = {"device": "cuda", "batch_size": 100, "normalize_embeddings": True}
+def fill_dnd_collection(text_splitter: any, embeddings: any) -> None:
+    # local
+    raw = parser.from_file("data/DnD-5e-Handbook.pdf")
+    print(len(raw['content']))
+    docs = text_splitter.create_documents([raw['content']])
+    vector_store = Milvus.from_documents(
+        docs,
+        embedding=embeddings,
+        connection_args={"host": "localhost", "port": 19530},
+        collection_name="dnd"
+    )
+    # remote
+    # loader = PyPDFLoader('https://orkerhulen.dk/onewebmedia/DnD%205e%20Players%20Handbook%20%28BnW%20OCR%29.pdf')
+    # data = loader.load()
 
-log_step(0, "Generate embeddings")
-embeddings = HuggingFaceBgeEmbeddings(
-    model_name=model_name,
-    model_kwargs=model_kwargs,
-    encode_kwargs=encode_kwargs,
-    query_instruction = "search_query:",
-    embed_instruction = "search_document:"
-)
+def generate_embeddings() -> any:
+    # model_name = "ibm/merlinite-7b"
+    # model_kwargs={"device": "cuda"},
+    # encode_kwargs = {"device": "cuda", "batch_size": 100, "normalize_embeddings": True}
+    model_name = "all-MiniLM-L6-v2"
+    model_kwargs = {"device": "cpu"}
+    encode_kwargs = {"normalize_embeddings": True}
+    embeddings = HuggingFaceBgeEmbeddings(
+        model_name=model_name,
+        # model_kwargs=model_kwargs,
+        encode_kwargs=encode_kwargs,
+        query_instruction = "search_query:",
+        embed_instruction = "search_document:"
+    )
+
+def generate_text_splitter(chunk_size=512, chunk_overlap=50) -> any:
+    # text_splitter = SemanticChunker(embeddings=embeddings) # fails 
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=chunk_size,
+        chunk_overlap=chunk_overlap,
+        length_function=len,
+        is_separator_regex=False
+    )
+    return text_splitter 
 
+log_step(0, "Generate embeddings")
+embeddings = generate_embeddings()
 log_step(1, "Init text splitter")
-text_splitter = SemanticChunker(embeddings=embeddings)
+text_splitter = generate_text_splitter()
 log_step(2, "Read Raw data from PDF")
-raw = parser.from_file("data/DnD-5e-Handbook.pdf")
 log_step(3, "Text splitting")
-print(len(raw['content']))
-docs = text_splitter.create_documents([raw['content']])
 log_step(4, "Log result")
-print(len(docs))
+fill_dnd_collection(embeddings=embeddings, text_splitter=text_splitter)
+
+
+# retreiver = vector_store.as_retreiver()
+# prompt = hub.pull("rlm/rag-prompt")