Update src/rag_utils.py

src/rag_utils.py

@@ -1,54 +1,54 @@
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_community.document_loaders import PyPDFLoader
-from langchain_chroma import Chroma
-from langchain_huggingface import HuggingFaceEmbeddings
-
-
-embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-
-def load_pdf_document(file_path):
-    document_loader = PyPDFLoader(file_path)
-    return document_loader.load()
-
-def chunk_documents(raw_documents):
-    text_processor = RecursiveCharacterTextSplitter(
-        chunk_size = 1000,
-        chunk_overlap = 200,
-        add_start_index = True
-    )
-    return text_processor.split_documents(raw_documents)
-
-def find_related_documents(query, vector_database):
-    # return vector_database.similarity_search(query, k=2)
-    return vector_database.max_marginal_relevance_search(query, k=2, fetch_k=5, lambda_mult=0.6)
-
-
-def ProcessDocuments(document_path: str) -> str:
-
-    loaded_doc = load_pdf_document(document_path)
-    chunked_doc = chunk_documents(loaded_doc)
-
-    
-    vector_database = Chroma(
-        persist_directory=f"./chroma_store/{document_path.split("\\")[-1].split(".")[0]}",
-        embedding_function=embedding_model
-    )
-
-    vector_database.add_documents(chunked_doc)
-
-
-def generate_context(query: str, file: str):
-
-    ProcessDocuments(file)
-
-    
-    vector_database = Chroma(
-        persist_directory=f"./chroma_store/{file.split("\\")[-1].split(".")[0]}",
-        embedding_function=embedding_model
-    )
-
-    relevant_docs = find_related_documents(query, vector_database)
-    context_text = "\n".join([doc.page_content for doc in relevant_docs])
- 
-    return query, context_text
-
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_chroma import Chroma
+from langchain_huggingface import HuggingFaceEmbeddings
+
+
+embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+
+def load_pdf_document(file_path):
+    document_loader = PyPDFLoader(file_path)
+    return document_loader.load()
+
+def chunk_documents(raw_documents):
+    text_processor = RecursiveCharacterTextSplitter(
+        chunk_size = 1000,
+        chunk_overlap = 200,
+        add_start_index = True
+    )
+    return text_processor.split_documents(raw_documents)
+
+def find_related_documents(query, vector_database):
+    # return vector_database.similarity_search(query, k=2)
+    return vector_database.max_marginal_relevance_search(query, k=2, fetch_k=5, lambda_mult=0.6)
+
+
+def ProcessDocuments(document_path: str) -> str:
+
+    loaded_doc = load_pdf_document(document_path)
+    chunked_doc = chunk_documents(loaded_doc)
+
+    
+    vector_database = Chroma(
+        persist_directory=f"./chroma_store/{document_path.split("/")[-1].split(".")[0]}",
+        embedding_function=embedding_model
+    )
+
+    vector_database.add_documents(chunked_doc)
+
+
+def generate_context(query: str, file: str):
+
+    ProcessDocuments(file)
+
+    
+    vector_database = Chroma(
+        persist_directory=f"./chroma_store/{file.split("/")[-1].split(".")[0]}",
+        embedding_function=embedding_model
+    )
+
+    relevant_docs = find_related_documents(query, vector_database)
+    context_text = "\n".join([doc.page_content for doc in relevant_docs])
+ 
+    return query, context_text
+