New app

.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 dyf_w_embeddings.json filter=lfs diff=lfs merge=lfs -text
+rw7.json filter=lfs diff=lfs merge=lfs -text

app.py

@@ -11,18 +11,32 @@ from tenacity import retry, stop_after_attempt, wait_random_exponential
 
 
 #df = pd.read_json('https://www.dropbox.com/scl/fi/uh964d1k6woc9wo3l2slc/dyf_w_embeddings.json?rlkey=j23j5338n4e88kvvsmj7s7aff&dl=1')
-df = pd.read_json('dyf_w_embeddings.json')
-
+df = pd.read_json('rw7.json')
 
 GPT_MODEL = 'gpt-3.5-turbo'
 EMBEDDING_MODEL = "text-embedding-ada-002"
+@retry(wait=wait_random_exponential(min=1, max=5), stop=stop_after_attempt(6))
+def ask_naive(query):
+    messages = [
+        {"role": "system", "content": "You are a collegee sociology professor. Provide a very brief answer to this student question."},
+        {"role": "user", "content": query},
+    ]
+    response = openai.ChatCompletion.create(
+        model='gpt-3.5-turbo',
+        messages=messages,
+    )
+    
+    response_message = response["choices"][0]["message"]["content"]
+    return response_message
+
+# search function
 
 # search function
 def strings_ranked_by_relatedness(
     query: str,
     df: pd.DataFrame,
     relatedness_fn=lambda x, y: 1 - spatial.distance.cosine(x, y),
-    top_n: int = 25
+    top_n: int = 100
 ) -> tuple[list[str], list[float]]:
     """Returns a list of strings and relatednesses, sorted from most related to least."""
     query_embedding_response = openai.Embedding.create(
@@ -31,152 +45,76 @@ def strings_ranked_by_relatedness(
     )
     query_embedding = query_embedding_response["data"][0]["embedding"]
     strings_and_relatednesses = [
-        (row["citation"]+':\n'+row["text"]+'\nINDEX:'+str(i), relatedness_fn(query_embedding, row["embedding"]))
+        (row["text"], relatedness_fn(query_embedding, row["embedding"]))
         for i, row in df.iterrows()
     ]
     strings_and_relatednesses.sort(key=lambda x: x[1], reverse=True)
     strings, relatednesses = zip(*strings_and_relatednesses)
-    return strings[:top_n], relatednesses[:top_n]
+    return strings[:top_n]
 
-def num_tokens(text: str, model: str = GPT_MODEL) -> int:
+def num_tokens(text: str) -> int:
     """Return the number of tokens in a string."""
-    encoding = tiktoken.encoding_for_model(model)
+    encoding = tiktoken.encoding_for_model('gpt-3.5-turbo')
     return len(encoding.encode(text))
 
-def double_check(question, passage):
-    
-    message = f'Possibly related text:{passage}\n\nSearch query: {question}'
-    messages = [
-        {"role": "system", "content": "Is the following text topically related to the search query. Answer with just Yes or No."},
-        {"role": "user", "content": message},
-    ]
-    response = openai.ChatCompletion.create(
-        model='gpt-3.5-turbo',
-        messages=messages,
-        temperature=0
-    )
-    response_message = response["choices"][0]["message"]["content"]
-    if 'yes' in response_message.lower():
-        return True
-    return False
-    return response_message
-
-def extract_numbers_after_index(text):
-    numbers = []
-    lines = text.split("\n")
-
-    for line in lines:
-        if "INDEX:" in line:
-            index = line.split("INDEX:")[1].strip()
-            try:
-                number = int(index)
-                numbers.append(number)
-            except ValueError:
-                pass
-
-    return numbers
-
-
-def query_message(
-    query: str,
-    df: pd.DataFrame,
-    model: str,
-    token_budget: int
-) -> str:
-    """Return a message for GPT, with relevant source texts pulled from a dataframe."""
-    strings, relatednesses = strings_ranked_by_relatedness(query, df)
-    introduction = 'Use the below articles written by W.E.B. Du Bois subsequent question. Write your response in the form of an four paragraph essay for a college class. If the answer cannot be found in the articles, write "I could not find an answer. Be sure to put direct quotations in quotation marks. Use in APA-Style text references where approriate.'
-    message = introduction
-    article_cites = defaultdict(int)
-    
-    for counter, string in enumerate(strings):
-        article_cite = string.splitlines()[0]
-        next_article = f'\n\nDu Bois article:\n"""\n{string}\n"""'
+def build_resources(psuedo_answer):
+    related_book_selections = strings_ranked_by_relatedness(psuedo_answer, df, top_n=15)
+    message  = 'Real World Sociology selections:\n'
+    for selection in related_book_selections:
         if (
-            num_tokens(message + next_article + query, model=model)
-            > token_budget
+            num_tokens(message + selection)
+            > 3000
         ):
             break
         else:
-            if double_check(query, string) == True and article_cites[article_cite] <= 2:
-                message += next_article
-                article_cites[article_cite] += 1
-    print(article_cites)     
-    return message + query
-
-def remove_lines_with_index(input_string):
-    lines = input_string.strip().split('\n')
-    cleaned_lines = [line for line in lines if "INDEX:" not in line]
-    cleaned_string = "\n".join(cleaned_lines)
-    return cleaned_string
-
-def ask(
-    query: str,
-
-) -> str:
-    """Answers a query using GPT and a dataframe of relevant texts and embeddings."""
+            message += '\n' + selection
+    print(num_tokens(message))
+    return message
     
-    model = GPT_MODEL
-    token_budget = 4096 - 600
-
+@retry(wait=wait_random_exponential(min=1, max=5), stop=stop_after_attempt(6))
+def respond(question, textbook_samples):
+    messages = [
+        {"role": "system", "content": "You are a college profesor who excels at explaining topics to students. Start with a direct answer to the question. Then, definition/overview of the concept's essence; break it down into understandable pieces; use clear language and structure. Where approriate, provide connections  and comparisions to  related terms. "},
+        {"role": "user", "content": f"""Use markdown and emphasize important phrases in bold. Respond to the following question: {question}.
         
-    message = query_message(query, df, model=model, token_budget=token_budget)
+        When contructing the question, use the following information from the textbook.
+        {textbook_samples}
+        """ }
+        ]
     
-    # Add references
-    cite_rows = extract_numbers_after_index(message)
-    used_df = df[df.index.isin(cite_rows)].copy()
-    citations = list(set(used_df['citation'].values))
-    if len(citations) == 0:
-        return "No relevant articles found. Sorry. Please try a different question."
-    
-    resources = '**Resources**\n* ' + '\n* '.join(sorted(citations))
-    # clean up to remove index
-    message = remove_lines_with_index(message)
-    
-    
-    messages = [
-        {"role": "system", "content": "You answer questions based on the writings of W.E.B. Du Bois. All the provided texts are written by Du Bois."},
-        {"role": "user", "content": message},
-    ]
     response = openai.ChatCompletion.create(
-        model=model,
-        messages=messages,
-        temperature=0
-    )
-    response_message = response["choices"][0]["message"]["content"]
-    
-
+        model='gpt-3.5-turbo',
+        n=1,
+        messages=messages)
+    return response["choices"][0]["message"]["content"]
+        
+def ask(query):
+    psuedo_answer = ask_naive(query)
+    resources = build_resources(psuedo_answer)
+    response = respond(query, resources)
     
-    answer = f'{resources}\n\n**Summary**\n\n{response_message}'
-    return answer
+    return response
 
 
 
 intro_text = '''
-# W.E.B. Du Bois in the Crisis
-
-This search engine find the most relevant articles from [Dare You Fight](https://www.dareyoufight.org), an online repository of W.E.B. Du Bois's writings in The Crisis, the official journal of the NAACP, which Du Bois founded and edited between 1911 and 1934. In addition to locating the most relevant articles, it also produces a short essay in response to your question. 
-
-**Notes:** 
-* Avoid using "Du Bois" in the question, as this is information is passed along behind the scenes. 
-* Searches can take 20 to 40 seconds.
-* You may need a follow up question if your original question is only a word or two.
-* The model usually looks at five or fewer relevant articles, so if you response requires more, consider refining and splitting up your question. 
-
-**Caveats:** Like all apps that employ large language models, this one has the possiblitiy for bias and confabulation. Please refer to the original articles. 
+# Ask the textbook
 
+This app responds to your questions by looking up the most relevant selections from the textbook, and asking ChatGPT to respond based on the selections. It can take up to 30 seconds to respond.
 '''
 
 outro_text = '''
+**Caveats:** Like all apps that employ large language models, this one has the possiblitiy for bias and confabulation. 
+
 **Behind the Scenes**
 
 This app uses sentence embeddings and a large language model to craft the response. Behind the scenes, it involves the following steps:
 
-1. Each article from Dare You Fight (or segment of the article if it's long) is converted into a fixed-length vector representation using OpenAI's text-embedding-ada-002 model. These representations are stored in a dataframe.
-2. The user's query is embedded using the same text-embedding-ada-002 model to convert it into a fixed-length vector representation.
-3. To find the most relevant articles to the query, cosine similarity is calculated between the query vector and all the article vectors. The articles with the highest cosine similarity are retrieved as the top matches.
-4. The text of each of the possibly related articles (based on Step 3) is passed to OpenAI's ChatGPT 3.5 model, along with a question asking whether the text is relevant to the search query. Only texts coded as relevant are used in subsequent steps.
-5. All of the relevant texts (from Step 4), along with the original search query, are passed to OpenAI's ChatGPT 3.5 model with specific instructions to answer the query in the form of a college essay using only the supplied texts.
+1. Each page from the textbook (or segment of the article if it's long) is converted into a fixed-length vector representation using OpenAI's text-embedding-ada-002 model. These representations are stored in a dataframe.
+2. Your question is embedded using the same text-embedding-ada-002 model to convert it into a fixed-length vector representation.
+3. To find the most relevant articles to your question, cosine similarity is calculated between the query vector and all the page vectors. The pages with the highest cosine similarity are retrieved as the top matches.
+5. All of the relevant texts (from Step 3), along with the original search query, are passed to OpenAI's ChatGPT 3.5 model with specific instructions to answer the question using  the supplied texts.
+
 '''
 
 
@@ -188,16 +126,15 @@ with block:
         
     # Define the input and output blocks
     input_block = gr.Textbox(label='Question')
-    research_btn = gr.Button(value="Ask the archive")
+    research_btn = gr.Button(value="Ask the textbook")
     output_block = gr.Markdown(label="Response")
     research_btn.click(ask, inputs=input_block, outputs=output_block)
-    gr.Examples(["What is the relationship between social, political and economic equality?", 
-                 "What is Pan-Africanism?",
-                 "Did Du Bois support American involvement in WWI?",
-                 "What are the most effective tactics or methods for racial equality?",
-                 "Why was the NAACP founded and what was it's original goals?"], inputs=[input_block])
+    gr.Examples(["What is the difference beween organic and mechnical solidarity?", 
+                 ], inputs=[input_block])
     gr.Markdown(outro_text)
 
+
+
 # Launch the interface
 block.launch()
 

rw7.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1e4437c739095717327abd9f6a92f8057fe466b2e4e4b74070282d1294128755
+size 78978588