multiple files option

app.py

@@ -1,137 +1,134 @@
-import os
-from typing import List
-from chainlit.types import AskFileResponse
-from aimakerspace.text_utils import CharacterTextSplitter, TextFileLoader
-from aimakerspace.openai_utils.prompts import (
-    UserRolePrompt,
-    SystemRolePrompt,
-    AssistantRolePrompt,
-)
-from aimakerspace.openai_utils.embedding import EmbeddingModel
-from aimakerspace.vectordatabase import VectorDatabase
-from aimakerspace.openai_utils.chatmodel import ChatOpenAI
-import chainlit as cl
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-# from langchain_experimental.text_splitter import SemanticChunker
-# from langchain_openai.embeddings import OpenAIEmbeddings
-
-system_template = """\
-Use the following context to answer a users question. If you cannot find the answer in the context, say you don't know the answer."""
-system_role_prompt = SystemRolePrompt(system_template)
-
-user_prompt_template = """\
-Context:
-{context}
-Question:
-{question}
-"""
-user_role_prompt = UserRolePrompt(user_prompt_template)
-
-class RetrievalAugmentedQAPipeline:
-    def __init__(self, llm: ChatOpenAI(), vector_db_retriever: VectorDatabase) -> None:
-        self.llm = llm
-        self.vector_db_retriever = vector_db_retriever
-
-    async def arun_pipeline(self, user_query: str):
-        context_list = self.vector_db_retriever.search_by_text(user_query, k=4)
-
-        context_prompt = ""
-        for context in context_list:
-            context_prompt += context[0] + "\n"
-
-        formatted_system_prompt = system_role_prompt.create_message()
-
-        formatted_user_prompt = user_role_prompt.create_message(question=user_query, context=context_prompt)
-
-        async def generate_response():
-            async for chunk in self.llm.astream([formatted_system_prompt, formatted_user_prompt]):
-                yield chunk
-
-        return {"response": generate_response(), "context": context_list}
-
-text_splitter = RecursiveCharacterTextSplitter()
-# try:
-#     api_key = os.environ["OPENAI_API_KEY"]
-# except KeyError:
-#     print("Environment variable OPENAI_API_KEY not found")
-# text_splitter = SemanticChunker(OpenAIEmbeddings(api_key=api_key), breakpoint_threshold_type="standard_deviation")
-
-def process_text_file(file: AskFileResponse):
-    import tempfile
-    from langchain_community.document_loaders.pdf import PyPDFLoader
-
-    with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=file.name) as temp_file:
-        temp_file_path = temp_file.name
-
-    with open(temp_file_path, "wb") as f:
-        f.write(file.content)
-
-    if file.type == 'text/plain':
-        text_loader = TextFileLoader(temp_file_path)
-        documents = text_loader.load_documents()
-    elif file.type == 'application/pdf':
-        pdf_loader = PyPDFLoader(temp_file_path)
-        documents = pdf_loader.load()
-    else:
-        raise ValueError("Provide a .txt or .pdf file")
-    texts = [x.page_content for x in text_splitter.transform_documents(documents)]
-    # texts = [x.page_content for x in text_splitter.split_documents(documents)]
-    return texts
-
-
-
-@cl.on_chat_start
-async def on_chat_start():
-    files = None
-
-    # Wait for the user to upload a file
-    while files == None:
-        files = await cl.AskFileMessage(
-            content="Please upload a Text file or a PDF to begin!",
-            accept=["text/plain", "application/pdf"],
-            max_size_mb=12,
-            timeout=180,
-        ).send()
-
-    file = files[0]
-
-    msg = cl.Message(
-        content=f"Processing `{file.name}`...", disable_human_feedback=True
-    )
-    await msg.send()
-
-    # load the file
-    texts = process_text_file(file)
-
-    print(f"Processing {len(texts)} text chunks")
-
-    # Create a dict vector store
-    vector_db = VectorDatabase()
-    vector_db = await vector_db.abuild_from_list(texts)
-    
-    chat_openai = ChatOpenAI()
-
-    # Create a chain
-    retrieval_augmented_qa_pipeline = RetrievalAugmentedQAPipeline(
-        vector_db_retriever=vector_db,
-        llm=chat_openai
-    )
-    
-    # Let the user know that the system is ready
-    msg.content = f"Processing `{file.name}` done. You can now ask questions!"
-    await msg.update()
-
-    cl.user_session.set("chain", retrieval_augmented_qa_pipeline)
-
-
-@cl.on_message
-async def main(message):
-    chain = cl.user_session.get("chain")
-
-    msg = cl.Message(content="")
-    result = await chain.arun_pipeline(message.content)
-
-    async for stream_resp in result["response"]:
-        await msg.stream_token(stream_resp)
-
+import os
+from typing import List
+from chainlit.types import AskFileResponse
+from aimakerspace.text_utils import CharacterTextSplitter, TextFileLoader
+from aimakerspace.openai_utils.prompts import (
+    UserRolePrompt,
+    SystemRolePrompt,
+    AssistantRolePrompt,
+)
+from aimakerspace.openai_utils.embedding import EmbeddingModel
+from aimakerspace.vectordatabase import VectorDatabase
+from aimakerspace.openai_utils.chatmodel import ChatOpenAI
+import chainlit as cl
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+# from langchain_experimental.text_splitter import SemanticChunker
+# from langchain_openai.embeddings import OpenAIEmbeddings
+
+system_template = """\
+Use the following context to answer a users question. If you cannot find the answer in the context, say you don't know the answer."""
+system_role_prompt = SystemRolePrompt(system_template)
+
+user_prompt_template = """\
+Context:
+{context}
+Question:
+{question}
+"""
+user_role_prompt = UserRolePrompt(user_prompt_template)
+
+class RetrievalAugmentedQAPipeline:
+    def __init__(self, llm: ChatOpenAI(), vector_db_retriever: VectorDatabase) -> None:
+        self.llm = llm
+        self.vector_db_retriever = vector_db_retriever
+
+    async def arun_pipeline(self, user_query: str):
+        context_list = self.vector_db_retriever.search_by_text(user_query, k=4)
+
+        context_prompt = ""
+        for context in context_list:
+            context_prompt += context[0] + "\n"
+
+        formatted_system_prompt = system_role_prompt.create_message()
+
+        formatted_user_prompt = user_role_prompt.create_message(question=user_query, context=context_prompt)
+
+        async def generate_response():
+            async for chunk in self.llm.astream([formatted_system_prompt, formatted_user_prompt]):
+                yield chunk
+
+        return {"response": generate_response(), "context": context_list}
+
+text_splitter = RecursiveCharacterTextSplitter()
+
+
+def process_text_file(file: AskFileResponse):
+    import tempfile
+    from langchain_community.document_loaders.pdf import PyPDFLoader
+
+    with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=file.name) as temp_file:
+        temp_file_path = temp_file.name
+
+    with open(temp_file_path, "wb") as f:
+        f.write(file.content)
+
+    if file.type == 'text/plain':
+        text_loader = TextFileLoader(temp_file_path)
+        documents = text_loader.load_documents()
+    elif file.type == 'application/pdf':
+        pdf_loader = PyPDFLoader(temp_file_path)
+        documents = pdf_loader.load()
+    else:
+        raise ValueError("Provide a .txt or .pdf file")
+    texts = [x.page_content for x in text_splitter.transform_documents(documents)]
+    # texts = [x.page_content for x in text_splitter.split_documents(documents)]
+    return texts
+
+
+
+@cl.on_chat_start
+async def on_chat_start():
+    files = None
+
+    # Wait for the user to upload a file
+    while files == None:
+        files = await cl.AskFileMessage(
+            content="Please upload a Text file or a PDF to begin!",
+            accept=["text/plain", "application/pdf"],
+            max_size_mb=12,
+            timeout=180,
+            max_files=10
+        ).send()
+
+    file = files[0]
+
+    msg = cl.Message(
+        content=f"Processing `{file.name}`...", disable_human_feedback=True
+    )
+    await msg.send()
+
+    # load the file
+    texts = process_text_file(file)
+
+    print(f"Processing {len(texts)} text chunks")
+
+    # Create a dict vector store
+    vector_db = VectorDatabase()
+    vector_db = await vector_db.abuild_from_list(texts)
+    
+    chat_openai = ChatOpenAI()
+
+    # Create a chain
+    retrieval_augmented_qa_pipeline = RetrievalAugmentedQAPipeline(
+        vector_db_retriever=vector_db,
+        llm=chat_openai
+    )
+    
+    # Let the user know that the system is ready
+    msg.content = f"Processing `{file.name}` done. You can now ask questions!"
+    await msg.update()
+
+    cl.user_session.set("chain", retrieval_augmented_qa_pipeline)
+
+
+@cl.on_message
+async def main(message):
+    chain = cl.user_session.get("chain")
+
+    msg = cl.Message(content="")
+    result = await chain.arun_pipeline(message.content)
+
+    async for stream_resp in result["response"]:
+        await msg.stream_token(stream_resp)
+
     await msg.send()