new updates

xpertai/prompts.py

@@ -27,7 +27,7 @@
 - Then, provide a summary of everything you described previously to describe the relationship between these features and the {observation}. You must sound like a scientist.
   Give scientific evidence for these answers and provide citations.
 
-- Finally, provide the list of references used to answer. Use APA citation style for referencing. \n
+- Finally, provide the list of references only used to answer. DO NOT make up references. Use APA style for referencing. \n
 Eg: References: \n
     1. reference 1 \n
     2. reference 2 \n

xpertai/tools/explain_model.py

@@ -10,6 +10,7 @@ def get_modelsummary(arg_dict):
     "model_type":<classifier or regressor>,
     "top_k":<Number of features to explain>,
     "XAI_tool": <SHAP, LIME or Both>
+    "persist_directory":<path to vectordb>
     }.
 
     Example:
@@ -22,6 +23,9 @@ def get_modelsummary(arg_dict):
 
     '''
     save_dir = './data'
+    global persist_directory ; persist_directory = None
+    global clean ; clean = True
+
     if not os.path.exists(save_dir):
         os.mkdir(save_dir)
     # arg_dict = json.loads(json_request)
@@ -70,7 +74,8 @@ def get_modelsummary(arg_dict):
     f.close()
     metadata = {'Authors': 'XpertAI', 'Year': '2023', 'Title': 'XAI Summary'}
 
-    vector_db(lit_file=f'{save_dir}/XAI_summary.txt', clean=True,
+    vector_db(persist_directory=persist_directory,
+              lit_file=f'{save_dir}/XAI_summary.txt', clean=clean,
               metadatas=metadata)
 
     # Step 5: Generate summary of model explanation

xpertai/tools/generate_nle.py

@@ -11,6 +11,7 @@ def gen_nle(arg_dict):
     {"observation":<target property>,
     "XAI_tool": <SHAP, LIME or Both>,
     "top_k":<maximum number of features to explain>
+    "persist_directory":<path to vectordb>
     }
 
     Example:
@@ -20,6 +21,7 @@ def gen_nle(arg_dict):
     '''
 
     save_dir = './data'
+    global persist_directory ; persist_directory = "./data/chroma/"
     # arg_dict = json.loads(json_request)
     for k, val in arg_dict.items():
         globals()[k] = val
@@ -61,7 +63,7 @@ def gen_nle(arg_dict):
     # get human interpretable feature labels
     # #initiate retriever, chain
     llm = ChatOpenAI(
-        temperature=0.1,
+        temperature=0.0,
         model_name="gpt-4",
         request_timeout=1000)
 
@@ -78,30 +80,29 @@ def gen_nle(arg_dict):
     # generate NLEs with citations
 
     features = ','.join(new_labels)
+    db = Chroma(persist_directory=persist_directory,
+                    embedding_function=embedding)
     docs = []
     # first collect docs for each feature
     for feature in new_labels:
-        initial_question = f"""It has been identified by XAI analysis {feature} have an impact on the {observation}. \n
-        Your task is to explain how the {observation} is affected by the {feature}. \
-        How does each of these features impact the {observation}?
-        """
+        initial_question = f"""How does the {feature} impact the {observation}?"""
         # Get relevant docs
-
-        db = Chroma(persist_directory="./data/chroma/",
-                    embedding_function=embedding)
-
-        docs.append(db.max_marginal_relevance_search(initial_question))
+        fetched = db.max_marginal_relevance_search(initial_question,k=4)
+        docs.append(fetched)
 
     # flatten list of docs
     docs = [item for sublist in docs for item in sublist]
+
     # add citations from metadata
     documents = ""
     for i in range(len(docs)):
         doc = docs[i].page_content
         try:
             authors = docs[i].metadata["authors"]
             year = docs[i].metadata["year"]
-            documents += f"{doc} ({authors},{year}) \n\n"
+            title = docs[i].metadata["source"]
+            documents += f"{doc} REFERENCE:({authors},{year},{title}) \n\n"
+
         except BaseException:
             documents += f"{doc} \n\n"
 

xpertai/tools/utils.py

@@ -271,8 +271,8 @@ def explain_lime(df_init, model_path, model_type, top_k=2,
 
 def load_split_docs(filename, meta_data=None):
     r_splitter = RecursiveCharacterTextSplitter(
-        chunk_size=1500,
-        chunk_overlap=200,
+        chunk_size=500,
+        chunk_overlap=50,
         length_function=len
     )
     docs = None
@@ -293,8 +293,8 @@ def load_split_docs(filename, meta_data=None):
     return docs_split
 
 
-def _create_vecdb(docs_split, persist_directory):
-    embedding = OpenAIEmbeddings()
+def _create_vecdb(docs_split, persist_directory, embedding=None):
+    if embedding is None: embedding = OpenAIEmbeddings()
 
     vectordb = Chroma.from_documents(
         documents=docs_split,
@@ -304,8 +304,9 @@ def _create_vecdb(docs_split, persist_directory):
     vectordb.persist()
 
 
-def _update_vecdb(docs_split, persist_directory):
-    embedding = OpenAIEmbeddings()
+def _update_vecdb(docs_split, persist_directory,embedding=None):
+    if embedding is None: embedding = OpenAIEmbeddings()
+
     vectordb = Chroma(persist_directory=persist_directory,
                       embedding_function=embedding)
 
@@ -340,7 +341,8 @@ def _get_metadata(lit_file):
 def vector_db(persist_directory=None,
               lit_file=None, clean=False,
               try_meta_data=False,
-              metadatas=None):
+              metadatas=None,
+              embedding=None):
 
     if persist_directory is None:
         persist_directory = "./data/chroma/"
@@ -355,7 +357,7 @@ def vector_db(persist_directory=None,
         if os.path.exists(persist_directory):
             shutil.rmtree(persist_directory)
         os.mkdir(persist_directory)
-        _create_vecdb(text_split, persist_directory)
+        _create_vecdb(text_split, persist_directory,embedding=embedding)
 
     else:
-        _update_vecdb(text_split, persist_directory)
+        _update_vecdb(text_split, persist_directory,embedding=embedding)