first v

.gitignore

@@ -0,0 +1 @@
+books

README.md

@@ -4,9 +4,9 @@ emoji: 💬
 colorFrom: yellow
 colorTo: purple
 sdk: gradio
-app_file: app.py
+app_file: query.py
 pinned: false
 license: mit
 ---
 
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+An example chatbot doing RAG to fetch context form documents using Astra DB

app.py

@@ -1,63 +0,0 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()

query.py

@@ -0,0 +1,144 @@
+import gradio as gr
+
+from langchain_astradb import AstraDBVectorStore
+
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough, RunnableLambda
+from langchain_core.messages import SystemMessage, AIMessage, HumanMessage
+from langchain_openai import OpenAIEmbeddings, ChatOpenAI
+
+import os
+
+prompt_template = os.environ.get("PROMPT_TEMPLATE")
+
+prompt = ChatPromptTemplate.from_messages([('system', prompt_template)])
+
+AI = False
+
+def ai_setup():
+    global llm, prompt_chain
+    llm = ChatOpenAI(model = "gpt-4o", temperature=0.8)
+    
+    if AI:
+        embedding = OpenAIEmbeddings()
+        vstore = AstraDBVectorStore(
+            embedding=embedding,
+            collection_name=os.environ.get("ASTRA_DB_COLLECTION"),
+            token=os.environ.get("ASTRA_DB_APPLICATION_TOKEN"),
+            api_endpoint=os.environ.get("ASTRA_DB_API_ENDPOINT"),
+        )
+
+        retriever = vstore.as_retriever(search_kwargs={'k': 10})
+    else:
+        retriever = RunnableLambda(just_read)
+
+    prompt_chain = (
+        {"context": retriever, "question": RunnablePassthrough()}
+        | RunnableLambda(format_context) 
+        | prompt
+        # | llm
+        # | StrOutputParser()
+    )
+
+def group_and_sort(documents):
+    grouped = {}
+    for document in documents:
+        title = document.metadata["Title"]
+        docs = grouped.get(title, [])
+        grouped[title] = docs
+        
+        docs.append((document.page_content, document.metadata["range"]))
+    
+    for title, values in grouped.items():
+        values.sort(key=lambda doc:doc[1][0])
+
+    for title in grouped:
+        text = ''
+        prev_last = 0
+        for fragment, (start, last) in grouped[title]:
+            if   start < prev_last:
+                text += fragment[prev_last-start:]
+            elif start == prev_last:
+                text += fragment
+            else:
+                text += ' [...] '
+                text += fragment
+            prev_last = last
+
+        grouped[title] = text
+                
+    return grouped
+        
+def format_context(pipeline_state):
+    """Print the state passed between Runnables in a langchain and pass it on"""
+
+    context = ''
+    documents = group_and_sort(pipeline_state["context"])
+    for title, text in documents.items():
+        context += f"\nTitle: {title}\n"
+        context += text
+        context += '\n\n---\n'
+
+    pipeline_state["context"] = context
+    return pipeline_state
+
+def just_read(pipeline_state):
+    fname = "docs.pickle"
+    import pickle
+    
+    return pickle.load(open(fname, "rb"))
+
+def new_state():
+    return gr.State({
+        "system": None,
+    })
+
+def chat(message, history, state):    
+    if not history:
+        system_prompt = prompt_chain.invoke(message)
+        system_prompt = system_prompt.messages[0]
+        state["system"] = system_prompt
+    else:
+        system_prompt = state["system"]
+        
+    messages = [system_prompt]
+    for human, ai in history:
+        messages.append(HumanMessage(human))
+        messages.append(AIMessage(ai))
+    messages.append(HumanMessage(message))
+    
+    all = ''
+    for response in llm.stream(messages):
+        all += response.content
+        yield all
+
+def gr_main():
+    theme = gr.Theme.from_hub("freddyaboulton/dracula_revamped@0.3.9")
+    theme.set(
+        color_accent_soft="#818eb6",            # ChatBot.svelte / .message-row.panel.user-row
+        background_fill_secondary="#6272a4",    # ChatBot.svelte / .message-row.panel.bot-row
+        button_primary_text_color="*button_secondary_text_color",
+        button_primary_background_fill="*button_secondary_background_fill")
+
+    with gr.Blocks(
+        title="Sherlock Holmes stories",
+        fill_height=True,
+        theme=theme
+        ) as app:
+            state = new_state()
+            gr.ChatInterface(
+                chat,
+                chatbot=gr.Chatbot(show_label=False, render=False, scale=1),
+                title="Sherlock Holmes stories",
+                examples=[
+                    ["I arrived late last night and found a dead goose in my bed"],
+                    ["Help please sir. I'm about to get married, to the most lovely lady,"
+                    "and I just received a letter threatening me to make public some things"
+                    "of my past I'd rather keep quiet, unless I don't marry"],
+                ],
+            additional_inputs=[state])
+    app.launch(show_api=False)
+if __name__ == "__main__":
+    ai_setup()
+    gr_main()

requirements.txt

@@ -1 +1 @@
-huggingface_hub==0.22.2
+ragstack-ai