[Release] version 0.1.2

What's changed:

- Refactored the `chatbot/chatui.py` to consolidate methods into `ask_model()`.
- Updated the element names in the `result.html` template.
- Cleaned up the code related to reading and replacing custom error messages.
- Added more comments describing each prompt in the `ask_model()` method.
- Moved the role of `condition.txt` to `config.yaml`.
- Updated the Docs Agent module to read custom prompt text from `config.yaml`.
- Removed the `condition.txt` file.
- Re-generated the `poetry.lock` file to be up to date.

October 6, 2023

Author: kyolee415

demos/palm/python/docs-agent/README.md

@@ -57,7 +57,7 @@ content from the source documents given user questions.
 Once the most relevant content is returned, the Docs Agent server uses the prompt structure
 shown in Figure 3 to augment the user question with a preset **condition** and a list of
 **context**. (When the Docs Agent server starts, the condition value is read from the
-[`condition.txt`][condition-txt] file.) Then the Docs Agent server sends this prompt to a
+[`config.yaml`][condition-txt] file.) Then the Docs Agent server sends this prompt to a
 PaLM 2 model using the PaLM API and receives a response generated by the model.
 
 ![Docs Agent prompt strcture](docs/images/docs-agent-prompt-structure-01.png)
@@ -202,16 +202,16 @@ by the PaLM model:
 - Additional condition (for fact-checking):
 
   ```
-  Compare the following body of text to the context provided in this prompt and write
-  a short message that warns the readers about which part of the text below they
-  should consider fact-checking for themselves? (please keep your response concise and
-  mention only one important point):
+  Can you compare the following text to the context provided in this prompt and write
+  a short message that warns the readers about which part of the text they should
+  consider fact-checking? (Please keep your response concise and focus on only
+  one important item.)
   ```
 
 - Previously generated response
 
   ```
-  <RESPONSE_RETURNED_FROM_THE_PREVIOUS_PROMPT>
+  Text: <RESPONSE_RETURNED_FROM_THE_PREVIOUS_PROMPT>
   ```
 
 This "fact-checking" prompt returns a response similar to the following example:
@@ -542,15 +542,7 @@ allowing you to easily bring up and destory the Flask app instance.
 
 To customize settings in the Docs Agent chat app, do the following:
 
-1. (**Optional**) Update the `condition.txt` file to provide a more specific prompt condition
-   for your custom dataset, for example:
-
-   ```
-   You are a helpful chatbot answering questions from developers working on Flutter apps.
-   Read the following context first and answer the question at the end:
-   ```
-
-2. Edit the `config.yaml` file to update the following field:
+1. Edit the `config.yaml` file to update the following field:
 
    ```
    product_name: "My product"
@@ -563,6 +555,14 @@ To customize settings in the Docs Agent chat app, do the following:
    product_name: "Flutter"
    ```
 
+2. (**Optional**) Edit the `config.yaml` file to provide a more specific prompt
+   condition for your custom dataset, for example:
+
+   ```
+   condition_text: "You are a helpful chatbot answering questions from developers working on
+   Flutter apps. Read the following context first and answer the question at the end:"
+   ```
+
 ### 2. Launch the Docs Agent chat app
 
 To launch the Docs Agent chat app, do the following:
@@ -636,7 +636,7 @@ Meggin Kearney (`@Meggin`), and Kyo Lee (`@kyolee415`).
 [set-up-docs-agent]: #set-up-docs-agent
 [markdown-to-plain-text]: ./scripts/markdown_to_plain_text.py
 [populate-vector-database]: ./scripts/populate_vector_database.py
-[condition-txt]: ./condition.txt
+[condition-txt]: ./config.yaml
 [context-source-01]: http://eventhorizontelescope.org
 [fact-check-section]: #using-a-palm-2-model-to-fact-check-its-own-response
 [related-questions-section]: #using-a-palm-2-model-to-suggest-related-questions

demos/palm/python/docs-agent/chatbot/chatui.py

@@ -112,152 +112,115 @@ def rewrite():
         return redirect(url_for("chatui.index"))
 
 
+# Render a response page when the user asks a question
+# using input text box.
 @bp.route("/result", methods=["GET", "POST"])
 def result():
     if request.method == "POST":
-        uuid_value = uuid.uuid1()
-        question_captured = request.form["question"]
-        query_result = docs_agent.query_vector_store(question_captured)
-        context = markdown.markdown(query_result.fetch_formatted(Format.CONTEXT))
-        context_with_prefix = docs_agent.add_instruction_to_context(context)
-        response_in_markdown = docs_agent.ask_text_model_with_context(
-            context_with_prefix, question_captured
-        )
-        if response_in_markdown is None:
-            response_in_markdown = (
-                "The PaLM API is not able to answer this question at the moment. "
-                "Try to rephrase the question and ask again."
-            )
-        response_in_html = markdown.markdown(response_in_markdown)
-        metadatas = markdown.markdown(
-            query_result.fetch_formatted(Format.CLICKABLE_URL)
-        )
-        fact_checked_answer_in_markdown = docs_agent.ask_text_model_to_fact_check(
-            context_with_prefix, response_in_markdown
-        )
-        if fact_checked_answer_in_markdown is None:
-            fact_checked_answer_in_markdown = (
-                "The PaLM API is not able to answer this question at the moment. "
-                "Try to rephrase the question and ask again."
-            )
-        fact_checked_answer_in_html = markdown.markdown(fact_checked_answer_in_markdown)
-        new_question = (
-            "What are 5 questions developers might ask after reading the context?"
-        )
-        related_questions = markdown.markdown(
-            docs_agent.ask_text_model_with_context(response_in_markdown, new_question)
-        )
-        soup = BeautifulSoup(related_questions, "html.parser")
-        for item in soup.find_all("li"):
-            if item.string is not None:
-                link = soup.new_tag(
-                    "a",
-                    href=url_for(
-                        "chatui.question", ask=urllib.parse.quote_plus(item.string)
-                    ),
-                )
-                link.string = item.string
-                item.string = ""
-                item.append(link)
-        related_questions = soup
-        fact_link = markdown.markdown(
-            query_result.fetch_nearest_formatted(Format.CLICKABLE_URL)
-        )
-        server_url = request.url_root.replace("http", "https")
-        # Log the question and response to the log file.
-        log_question(uuid_value, question_captured, response_in_markdown)
-        return render_template(
-            "chatui/index.html",
-            question=question_captured,
-            context=context,
-            context_with_prefix=context_with_prefix,
-            response_in_markdown=response_in_markdown,
-            response_in_html=response_in_html,
-            product=product,
-            metadatas=metadatas,
-            fact_checked_answer=fact_checked_answer_in_html,
-            fact_link=fact_link,
-            related_questions=related_questions,
-            server_url=server_url,
-            uuid=uuid_value,
-        )
+        question = request.form["question"]
+        return ask_model(question)
     else:
         return redirect(url_for("chatui.index"))
 
 
+# Render a response page when the user clicks a question
+# from the related questions list.
 @bp.route("/question/<ask>", methods=["GET", "POST"])
 def question(ask):
     if request.method == "GET":
-        uuid_value = uuid.uuid1()
-        question_captured = urllib.parse.unquote_plus(ask)
-        query_result = docs_agent.query_vector_store(question_captured)
-        context = markdown.markdown(query_result.fetch_formatted(Format.CONTEXT))
-        context_with_prefix = docs_agent.add_instruction_to_context(context)
-        response_in_markdown = docs_agent.ask_text_model_with_context(
-            context_with_prefix, question_captured
-        )
-        if response_in_markdown is None:
-            response_in_markdown = (
-                "The PaLM API is not able to answer this question at the moment. "
-                "Try to rephrase the question and ask again."
-            )
-        response_in_html = markdown.markdown(response_in_markdown)
-        metadatas = markdown.markdown(
-            query_result.fetch_formatted(Format.CLICKABLE_URL)
-        )
-        fact_checked_answer_in_markdown = docs_agent.ask_text_model_to_fact_check(
-            context_with_prefix, response_in_markdown
-        )
-        if fact_checked_answer_in_markdown is None:
-            fact_checked_answer_in_markdown = (
-                "The PaLM API is not able to answer this question at the moment. "
-                "Try to rephrase the question and ask again."
-            )
-        fact_checked_answer_in_html = markdown.markdown(fact_checked_answer_in_markdown)
-        new_question = (
-            "What are 5 questions developers might ask after reading the context?"
-        )
-        related_questions = markdown.markdown(
-            docs_agent.ask_text_model_with_context(response_in_markdown, new_question)
-        )
-        soup = BeautifulSoup(related_questions, "html.parser")
-        for item in soup.find_all("li"):
-            if item.string is not None:
-                link = soup.new_tag(
-                    "a",
-                    href=url_for(
-                        "chatui.question", ask=urllib.parse.quote_plus(item.string)
-                    ),
-                )
-                link.string = item.string
-                item.string = ""
-                item.append(link)
-        related_questions = soup
-        fact_link = markdown.markdown(
-            query_result.fetch_nearest_formatted(Format.CLICKABLE_URL)
-        )
-        server_url = request.url_root.replace("http", "https")
-        # Log the question and response to the log file.
-        log_question(uuid_value, question_captured, response_in_markdown)
-        return render_template(
-            "chatui/index.html",
-            question=question_captured,
-            context=context,
-            context_with_prefix=context_with_prefix,
-            response_in_markdown=response_in_markdown,
-            response_in_html=response_in_html,
-            product=product,
-            metadatas=metadatas,
-            fact_checked_answer=fact_checked_answer_in_html,
-            fact_link=fact_link,
-            related_questions=related_questions,
-            server_url=server_url,
-            uuid=uuid_value,
-        )
+        question = urllib.parse.unquote_plus(ask)
+        return ask_model(question)
     else:
         return redirect(url_for("chatui.index"))
 
 
+# Construct a set of prompts using the user question, send the prompts to
+# the lanaguage model, receive responses, and present them into a page.
+def ask_model(question):
+    ### PROMPT 1: AUGMENT THE USER QUESTION WITH CONTEXT.
+    # 1. Use the question to retrieve a list of related contents from the database.
+    # 2. Convert the list of related contents into plain Markdown text (context).
+    # 3. Add the custom condition text to the context.
+    # 4. Send the prompt (condition + context + question) to the language model.
+    query_result = docs_agent.query_vector_store(question)
+    context = markdown.markdown(query_result.fetch_formatted(Format.CONTEXT))
+    context_with_prefix = docs_agent.add_instruction_to_context(context)
+    response = docs_agent.ask_text_model_with_context(context_with_prefix, question)
+
+    ### PROMPT 2: FACT-CHECK THE PREVIOUS RESPONSE.
+    fact_checked_response = docs_agent.ask_text_model_to_fact_check(
+        context_with_prefix, response
+    )
+
+    ### PROMPT 3: GET 5 RELATED QUESTIONS.
+    # 1. Prepare a new question asking the model to come up with 5 related questions.
+    # 2. Ask the language model with the new question.
+    # 3. Parse the model's response into a list in HTML format.
+    new_question = (
+        "What are 5 questions developers might ask after reading the context?"
+    )
+    new_response = markdown.markdown(
+        docs_agent.ask_text_model_with_context(response, new_question)
+    )
+    related_questions = parse_related_questions_response_to_html_list(new_response)
+
+    ### RETRIEVE SOURCE URLS.
+    # - Construct clickable URLs using the returned related contents above.
+    # - Extract the URL of the top related content for the fact-check message.
+    clickable_urls = markdown.markdown(
+        query_result.fetch_formatted(Format.CLICKABLE_URL)
+    )
+    fact_check_url = markdown.markdown(
+        query_result.fetch_nearest_formatted(Format.CLICKABLE_URL)
+    )
+
+    ### PREPARE OTHER ELEMENTS NEEDED BY UI.
+    # - Create a uuid for this request.
+    # - Convert the first response from the model into HTML for rendering.
+    # - Convert the fact-check response from the model into HTML for rendering.
+    # - A workaround to get the server's URL to work with the rewrite and like features.
+    new_uuid = uuid.uuid1()
+    response_in_html = markdown.markdown(response)
+    fact_checked_response_in_html = markdown.markdown(fact_checked_response)
+    server_url = request.url_root.replace("http", "https")
+
+    ### LOG THIS REQUEST.
+    log_question(new_uuid, question, response)
+
+    return render_template(
+        "chatui/index.html",
+        question=question,
+        context=context,
+        response=response,
+        response_in_html=response_in_html,
+        clickable_urls=clickable_urls,
+        fact_checked_response_in_html=fact_checked_response_in_html,
+        fact_check_url=fact_check_url,
+        related_questions=related_questions,
+        product=product,
+        server_url=server_url,
+        uuid=new_uuid,
+    )
+
+
+# Parse a response containing a list of related questions from the language model
+# and convert it into an HTML-based list.
+def parse_related_questions_response_to_html_list(response):
+    soup = BeautifulSoup(response, "html.parser")
+    for item in soup.find_all("li"):
+        if item.string is not None:
+            link = soup.new_tag(
+                "a",
+                href=url_for(
+                    "chatui.question", ask=urllib.parse.quote_plus(item.string)
+                ),
+            )
+            link.string = item.string
+            item.string = ""
+            item.append(link)
+    return soup
+
+
 # Log the question and response to the server's log file.
 def log_question(uid, user_question, response):
     date_format = "%m/%d/%Y %H:%M:%S %Z"

demos/palm/python/docs-agent/chatbot/templates/chatui/result.html

@@ -8,9 +8,9 @@ <h2>PaLM's answer</h2>
     {{ response_in_html | safe }}
   </span>
   <h4>Important:</h4>
-  {{ fact_checked_answer | safe }}
+  {{ fact_checked_response_in_html | safe }}
   <p id="fact-check-url">To verify this information, please check out:</p>
-  {{ fact_link | safe }}
+  {{ fact_check_url | safe }}
 </div>
 <div class="related-questions">
   <h3>Related questions</h3>
@@ -25,7 +25,7 @@ <h2 class="handle">
     {{ context | safe }}
     <span class="reference-content">
       <h4>Reference:</h4>
-      {{ metadatas | safe }}
+      {{ clickable_urls | safe }}
     </span>
   </div>
 </section>
@@ -43,7 +43,7 @@ <h4 id="rewrite-response-header">PaLM's response:</h4>
     {{ response_in_html | safe }}
   </span>
   <h4>Rewrite:</h4>
-  <textarea id="edit-text-area">{{ response_in_markdown | safe }}</textarea>
+  <textarea id="edit-text-area">{{ response | safe }}</textarea>
   <label for="user-id">User ID:</label>
   <input type="text" id="user-id" name="user-id" placeholder="Optional">
   <br>