Updates after sync

app.py

@@ -38,7 +38,7 @@ def manage_files(modal, document_store):
     if modal.is_open():
         with modal.container():
             uploaded_file = st.file_uploader(
-                "Upload a CV in PDF format",
+                "Upload a document in PDF format",
                 type=("pdf",),
                 on_change=new_file(),
                 disabled=st.session_state['document_qa_model'] is None,
@@ -57,7 +57,7 @@ def manage_files(modal, document_store):
             if uploaded_file:
                 st.session_state['file_uploaded'] = True
                 st.session_state['files'] = pd.concat([st.session_state['files'], edited_df])
-                with st.spinner('Processing the CV content...'):
+                with st.spinner('Processing the document...'):
                     store_file_in_table(document_store, uploaded_file)
                     ingest_document(uploaded_file)
 
@@ -103,7 +103,7 @@ def init_session_state():
 
 def set_page_config():
     st.set_page_config(
-        page_title="CV Insights AI Assistant",
+        page_title="Document Insights AI Assistant",
         page_icon=":shark:",
         initial_sidebar_state="expanded",
         layout="wide",
@@ -121,7 +121,8 @@ def update_running_model(api_key, model):
 
 
 def init_api_key_dict():
-    st.session_state['models'] = OPENAI_MODELS + list(OPEN_MODELS) + ['local LLM']
+    # st.session_state['models'] = OPENAI_MODELS + list(OPEN_MODELS) + ['local LLM']
+    st.session_state['models'] = OPENAI_MODELS
     for model_name in OPENAI_MODELS:
         st.session_state['api_keys'][model_name] = None
 
@@ -158,8 +159,11 @@ def setup_model_selection():
             if model == 'local LLM':
                 st.session_state['document_qa_model'] = init_qa(model)
 
-    api_key = st.sidebar.text_input("Enter LLM-authorization Key:", type="password",
-                                    disabled=st.session_state['current_selected_model'] == 'local LLM')
+    # api_key = st.sidebar.text_input("Enter LLM-authorization Key:", type="password",
+    #                                 disabled=st.session_state['current_selected_model'] == 'local LLM')
+
+    api_key = "sk-proj-vQgkXQKYjy8m3waKtDFQT3BlbkFJ7uuMeDinKxql7J0Q161N"
+
     if api_key and api_key != st.session_state['current_api_key']:
         update_running_model(api_key, model)
         st.session_state['current_api_key'] = api_key
@@ -213,7 +217,7 @@ class StreamlitApp:
             # Sidebar for Task Selection
             st.sidebar.header('Options:')
             model = setup_model_selection()
-            setup_task_selection(model)
+            # setup_task_selection(model)
             st.divider()
             self.authenticator.logout()
             reset_chat_memory()

document_qa_engine.py

@@ -1,142 +1,142 @@
-from typing import List
-
-from haystack.dataclasses import ChatMessage
-from pypdf import PdfReader
-from haystack.utils import Secret
-from haystack import Pipeline, Document, component
-
-from haystack.components.preprocessors import DocumentCleaner, DocumentSplitter
-from haystack.components.writers import DocumentWriter
-from haystack.components.embedders import SentenceTransformersDocumentEmbedder, SentenceTransformersTextEmbedder
-from haystack.document_stores.in_memory import InMemoryDocumentStore
-from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
-from haystack.components.builders import DynamicChatPromptBuilder
-from haystack.components.generators.chat import OpenAIChatGenerator, HuggingFaceTGIChatGenerator
-from haystack.document_stores.types import DuplicatePolicy
-
-SENTENCE_RETREIVER_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
-
-MAX_TOKENS = 500
-
-template = """
-As a professional HR recruiter given the following information, answer the question shortly and concisely in 1 or 2 sentences.
-
-Context:
-{% for document in documents %}
-    {{ document.content }}
-{% endfor %}
-
-Question: {{question}}
-Answer:
-"""
-
-
-@component
-class UploadedFileConverter:
-    """
-    A component to convert uploaded PDF files to Documents
-    """
-
-    @component.output_types(documents=List[Document])
-    def run(self, uploaded_file):
-        pdf = PdfReader(uploaded_file)
-        documents = []
-        # uploaded file name without .pdf at the end and with _ and page number at the end
-        name = uploaded_file.name.rstrip('.PDF') + '_'
-        for page in pdf.pages:
-            documents.append(
-                Document(
-                    content=page.extract_text(),
-                    meta={'name': name + f"_{page.page_number}"}))
-        return {"documents": documents}
-
-
-def create_ingestion_pipeline(document_store):
-    doc_embedder = SentenceTransformersDocumentEmbedder(model=SENTENCE_RETREIVER_MODEL)
-    doc_embedder.warm_up()
-
-    pipeline = Pipeline()
-    pipeline.add_component("converter", UploadedFileConverter())
-    pipeline.add_component("cleaner", DocumentCleaner())
-    pipeline.add_component("splitter",
-                           DocumentSplitter(split_by="passage", split_length=100, split_overlap=10))
-    pipeline.add_component("embedder", doc_embedder)
-    pipeline.add_component("writer",
-                           DocumentWriter(document_store=document_store, policy=DuplicatePolicy.OVERWRITE))
-
-    pipeline.connect("converter", "cleaner")
-    pipeline.connect("cleaner", "splitter")
-    pipeline.connect("splitter", "embedder")
-    pipeline.connect("embedder", "writer")
-    return pipeline
-
-
-def create_inference_pipeline(document_store, model_name, api_key):
-    if model_name == "local LLM":
-        generator = OpenAIChatGenerator(api_key=Secret.from_token("<local LLM doesn't need an API key>"),
-                                        model=model_name,
-                                        api_base_url="http://localhost:1234/v1",
-                                        generation_kwargs={"max_tokens": MAX_TOKENS}
-                                        )
-    elif "gpt" in model_name:
-        generator = OpenAIChatGenerator(api_key=Secret.from_token(api_key), model=model_name,
-                                        generation_kwargs={"max_tokens": MAX_TOKENS},
-                                        streaming_callback=lambda x: print(x),
-                                        )
-    else:
-        generator = HuggingFaceTGIChatGenerator(token=Secret.from_token(api_key), model=model_name,
-                                                generation_kwargs={"max_new_tokens": MAX_TOKENS}
-                                                )
-    pipeline = Pipeline()
-    pipeline.add_component("text_embedder",
-                           SentenceTransformersTextEmbedder(model=SENTENCE_RETREIVER_MODEL))
-    pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store, top_k=3))
-    pipeline.add_component("prompt_builder",
-                           DynamicChatPromptBuilder(runtime_variables=["query", "documents"]))
-    pipeline.add_component("llm", generator)
-    pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
-    pipeline.connect("retriever.documents", "prompt_builder.documents")
-    pipeline.connect("prompt_builder.prompt", "llm.messages")
-
-    return pipeline
-
-
-class DocumentQAEngine:
-    def __init__(self,
-                 model_name,
-                 api_key=None
-                 ):
-        self.api_key = api_key
-        self.model_name = model_name
-        document_store = InMemoryDocumentStore()
-        self.chunks = []
-        self.inference_pipeline = create_inference_pipeline(document_store, model_name, api_key)
-        self.pdf_ingestion_pipeline = create_ingestion_pipeline(document_store)
-
-    def ingest_pdf(self, uploaded_file):
-        self.pdf_ingestion_pipeline.run({"converter": {"uploaded_file": uploaded_file}})
-
-    def inference(self, query, input_messages: List[dict]):
-        system_message = ChatMessage.from_system(
-            "You are a consultant answering questions about potential AI use cases based on the uploaded document. Please provide accurate, concise answers in 1-5 sentences, referencing the document content.")
-        messages = [system_message]
-        for message in input_messages:
-            if message["role"] == "user":
-                messages.append(ChatMessage.from_system(message["content"]))
-            else:
-                messages.append(
-                    ChatMessage.from_user(message["content"]))
-        messages.append(ChatMessage.from_user("""
-        Relevant information from the uploaded documents:
-            {% for doc in documents %}
-                {{ doc.content }}
-            {% endfor %}
-
-            \nQuestion: {{query}}
-            \nAnswer:
-        """))
-        res = self.inference_pipeline.run(data={"text_embedder": {"text": query},
-                                                "prompt_builder": {"prompt_source": messages,
-                                                                   "query": query
-                                                                   }})
-        return res["llm"]["replies"][0].content
+from typing import List
+
+from haystack.dataclasses import ChatMessage
+from pypdf import PdfReader
+from haystack.utils import Secret
+from haystack import Pipeline, Document, component
+
+from haystack.components.preprocessors import DocumentCleaner, DocumentSplitter
+from haystack.components.writers import DocumentWriter
+from haystack.components.embedders import SentenceTransformersDocumentEmbedder, SentenceTransformersTextEmbedder
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
+from haystack.components.builders import DynamicChatPromptBuilder
+from haystack.components.generators.chat import OpenAIChatGenerator, HuggingFaceTGIChatGenerator
+from haystack.document_stores.types import DuplicatePolicy
+
+SENTENCE_RETREIVER_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
+
+MAX_TOKENS = 500
+
+template = """
+As a professional HR recruiter given the following information, answer the question shortly and concisely in 1 or 2 sentences.
+
+Context:
+{% for document in documents %}
+    {{ document.content }}
+{% endfor %}
+
+Question: {{question}}
+Answer:
+"""
+
+
+@component
+class UploadedFileConverter:
+    """
+    A component to convert uploaded PDF files to Documents
+    """
+
+    @component.output_types(documents=List[Document])
+    def run(self, uploaded_file):
+        pdf = PdfReader(uploaded_file)
+        documents = []
+        # uploaded file name without .pdf at the end and with _ and page number at the end
+        name = uploaded_file.name.rstrip('.PDF') + '_'
+        for page in pdf.pages:
+            documents.append(
+                Document(
+                    content=page.extract_text(),
+                    meta={'name': name + f"_{page.page_number}"}))
+        return {"documents": documents}
+
+
+def create_ingestion_pipeline(document_store):
+    doc_embedder = SentenceTransformersDocumentEmbedder(model=SENTENCE_RETREIVER_MODEL)
+    doc_embedder.warm_up()
+
+    pipeline = Pipeline()
+    pipeline.add_component("converter", UploadedFileConverter())
+    pipeline.add_component("cleaner", DocumentCleaner())
+    pipeline.add_component("splitter",
+                           DocumentSplitter(split_by="passage", split_length=100, split_overlap=10))
+    pipeline.add_component("embedder", doc_embedder)
+    pipeline.add_component("writer",
+                           DocumentWriter(document_store=document_store, policy=DuplicatePolicy.OVERWRITE))
+
+    pipeline.connect("converter", "cleaner")
+    pipeline.connect("cleaner", "splitter")
+    pipeline.connect("splitter", "embedder")
+    pipeline.connect("embedder", "writer")
+    return pipeline
+
+
+def create_inference_pipeline(document_store, model_name, api_key):
+    if model_name == "local LLM":
+        generator = OpenAIChatGenerator(api_key=Secret.from_token("<local LLM doesn't need an API key>"),
+                                        model=model_name,
+                                        api_base_url="http://localhost:1234/v1",
+                                        generation_kwargs={"max_tokens": MAX_TOKENS}
+                                        )
+    elif "gpt" in model_name:
+        generator = OpenAIChatGenerator(api_key=Secret.from_token(api_key), model=model_name,
+                                        generation_kwargs={"max_tokens": MAX_TOKENS},
+                                        streaming_callback=lambda chunk: print(chunk.content, end="", flush=True),
+                                        )
+    else:
+        generator = HuggingFaceTGIChatGenerator(token=Secret.from_token(api_key), model=model_name,
+                                                generation_kwargs={"max_new_tokens": MAX_TOKENS}
+                                                )
+    pipeline = Pipeline()
+    pipeline.add_component("text_embedder",
+                           SentenceTransformersTextEmbedder(model=SENTENCE_RETREIVER_MODEL))
+    pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store, top_k=3))
+    pipeline.add_component("prompt_builder",
+                           DynamicChatPromptBuilder(runtime_variables=["query", "documents"]))
+    pipeline.add_component("llm", generator)
+    pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
+    pipeline.connect("retriever.documents", "prompt_builder.documents")
+    pipeline.connect("prompt_builder.prompt", "llm.messages")
+
+    return pipeline
+
+
+class DocumentQAEngine:
+    def __init__(self,
+                 model_name,
+                 api_key=None
+                 ):
+        self.api_key = api_key
+        self.model_name = model_name
+        document_store = InMemoryDocumentStore()
+        self.chunks = []
+        self.inference_pipeline = create_inference_pipeline(document_store, model_name, api_key)
+        self.pdf_ingestion_pipeline = create_ingestion_pipeline(document_store)
+
+    def ingest_pdf(self, uploaded_file):
+        self.pdf_ingestion_pipeline.run({"converter": {"uploaded_file": uploaded_file}})
+
+    def inference(self, query, input_messages: List[dict]):
+        system_message = ChatMessage.from_system(
+            "You are a consultant answering questions about potential AI use cases based on the uploaded document. Please provide accurate, concise answers in 3-5 sentences, referencing both the document content and additional sources.")
+        messages = [system_message]
+        for message in input_messages:
+            if message["role"] == "user":
+                messages.append(ChatMessage.from_system(message["content"]))
+            else:
+                messages.append(
+                    ChatMessage.from_user(message["content"]))
+        messages.append(ChatMessage.from_user("""
+        Relevant information from the uploaded documents:
+            {% for doc in documents %}
+                {{ doc.content }}
+            {% endfor %}
+
+            \nQuestion: {{query}}
+            \nAnswer:
+        """))
+        res = self.inference_pipeline.run(data={"text_embedder": {"text": query},
+                                                "prompt_builder": {"prompt_source": messages,
+                                                                   "query": query
+                                                                   }})
+        return res["llm"]["replies"][0].content

utils.py

@@ -50,7 +50,7 @@ def append_documentation_to_sidebar():
     with st.expander("Documentation"):
         st.markdown(
             """
-            Upload a CV as PDF document. Once the spinner stops, you can proceed to ask your questions. The answers will
+            Upload a document as a PDF document. Once the spinner stops, you can proceed to ask your questions. The answers will
             be displayed in the right column. The system will answer your questions using the content of the document
             and mark refrences over the PDF viewer.
             """)