use new hashing primitives

The `st.experimental_memo()` and `st.cache_data` methods are deprecated
so I replaced them with the new caching methods.

However, the `Document` class cannot be hashed, so I added a custom
hash function for it. Also, as a temporary solution, the `search_docs()` method was moved to the `main.py` file since the `VectorStore` class cannot be
hashed.

knowledge_gpt/main.py

@@ -9,7 +9,6 @@
     parse_docx,
     parse_pdf,
     parse_txt,
-    search_docs,
     text_to_docs,
     wrap_text_in_html,
 )
@@ -72,7 +71,7 @@ def clear_submit():
         st.session_state["submit"] = True
         # Output Columns
         answer_col, sources_col = st.columns(2)
-        sources = search_docs(index, query)
+        sources = index.similarity_search(query, k=5)
 
         try:
             answer = get_answer(sources, query)

knowledge_gpt/utils.py

@@ -16,16 +16,23 @@
 from knowledge_gpt.embeddings import OpenAIEmbeddings
 from knowledge_gpt.prompts import STUFF_PROMPT
 
+from hashlib import md5
 
-@st.experimental_memo()
+
+def hash_func(doc: Document) -> str:
+    """Hash function for caching Documents"""
+    return md5(doc.page_content.encode("utf-8")).hexdigest()
+
+
+@st.cache_data()
 def parse_docx(file: BytesIO) -> str:
     text = docx2txt.process(file)
     # Remove multiple newlines
     text = re.sub(r"\n\s*\n", "\n\n", text)
     return text
 
 
-@st.experimental_memo()
+@st.cache_data()
 def parse_pdf(file: BytesIO) -> List[str]:
     pdf = PdfReader(file)
     output = []
@@ -43,15 +50,15 @@ def parse_pdf(file: BytesIO) -> List[str]:
     return output
 
 
-@st.experimental_memo()
+@st.cache_data()
 def parse_txt(file: BytesIO) -> str:
     text = file.read().decode("utf-8")
     # Remove multiple newlines
     text = re.sub(r"\n\s*\n", "\n\n", text)
     return text
 
 
-@st.cache(allow_output_mutation=True)
+@st.cache_data()
 def text_to_docs(text: str | List[str]) -> List[Document]:
     """Converts a string or list of strings to a list of Documents
     with metadata."""
@@ -84,7 +91,7 @@ def text_to_docs(text: str | List[str]) -> List[Document]:
     return doc_chunks
 
 
-@st.cache(allow_output_mutation=True, show_spinner=False)
+@st.cache_data(show_spinner=False, hash_funcs={Document: hash_func})
 def embed_docs(docs: List[Document]) -> VectorStore:
     """Embeds a list of Documents and returns a FAISS index"""
 
@@ -103,17 +110,7 @@ def embed_docs(docs: List[Document]) -> VectorStore:
         return index
 
 
-@st.cache(allow_output_mutation=True)
-def search_docs(index: VectorStore, query: str) -> List[Document]:
-    """Searches a FAISS index for similar chunks to the query
-    and returns a list of Documents."""
-
-    # Search for similar chunks
-    docs = index.similarity_search(query, k=5)
-    return docs
-
-
-@st.cache(allow_output_mutation=True)
+@st.cache_data(show_spinner=False, hash_funcs={Document: hash_func})
 def get_answer(docs: List[Document], query: str) -> Dict[str, Any]:
     """Gets an answer to a question from a list of Documents."""
 
@@ -137,7 +134,7 @@ def get_answer(docs: List[Document], query: str) -> Dict[str, Any]:
     return answer
 
 
-@st.cache(allow_output_mutation=True)
+@st.cache_data(show_spinner=False, hash_funcs={Document: hash_func})
 def get_sources(answer: Dict[str, Any], docs: List[Document]) -> List[Document]:
     """Gets the source documents for an answer."""