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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tesla_db/chroma.sqlite3 filter=lfs diff=lfs merge=lfs -text

app.py

@@ -1,63 +1,131 @@
-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()
+import os
+import uuid
+import json
+
+import gradio as gr
+
+from openai import OpenAI
+
+from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings
+from langchain_community.vectorstores import Chroma
+
+from huggingface_hub import CommitScheduler
+from pathlib import Path
+
+
+client = OpenAI(
+    base_url="https://api.endpoints.anyscale.com/v1",
+    api_key=os.environ['ANYSCALE_API_KEY']
+)
+
+embedding_model = SentenceTransformerEmbeddings(model_name='thenlper/gte-small')
+
+tesla_10k_collection = 'tesla-10k-2019-to-2023'
+
+vectorstore_persisted = Chroma(
+    collection_name=tesla_10k_collection,
+    persist_directory='./tesla_db',
+    embedding_function=embedding_model
+)
+
+retriever = vectorstore_persisted.as_retriever(
+    search_type='similarity',
+    search_kwargs={'k': 5}
+)
+
+# Prepare the logging functionality
+
+log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
+log_folder = log_file.parent
+
+scheduler = CommitScheduler(
+    repo_id="document-qna-chroma-anyscale-logs",
+    repo_type="dataset",
+    folder_path=log_folder,
+    path_in_repo="data",
+    every=2
+)
+
+qna_system_message = """
+You are an assistant to a financial services firm who answers user queries on annual reports.
+Users will ask questions delimited by triple backticks, that is, ```.
+User input will have the context required by you to answer user questions.
+This context will begin with the token: ###Context.
+The context contains references to specific portions of a document relevant to the user query.
+Please answer only using the context provided in the input. However, do not mention anything about the context in your answer. 
+If the answer is not found in the context, respond "I don't know".
+"""
+
+qna_user_message_template = """
+###Context
+Here are some documents that are relevant to the question.
+{context}
+```
+{question}
+```
+"""
+
+# Define the predict function that runs when 'Submit' is clicked or when a API request is made
+def predict(user_input):
+
+    relevant_document_chunks = retriever.invoke(user_input)
+    context_list = [d.page_content for d in relevant_document_chunks]
+    context_for_query = ".".join(context_list)
+    
+    prompt = [
+        {'role':'system', 'content': qna_system_message},
+        {'role': 'user', 'content': qna_user_message_template.format(
+            context=context_for_query,
+            question=user_input
+            )
+        }
+    ]
+
+    try:
+        response = client.chat.completions.create(
+            model='mlabonne/NeuralHermes-2.5-Mistral-7B',
+            messages=prompt,
+            temperature=0
+        )
+
+        prediction = response.choices[0].message.content
+
+    except Exception as e:
+        prediction = e
+
+    # While the prediction is made, log both the inputs and outputs to a local log file
+    # While writing to the log file, ensure that the commit scheduler is locked to avoid parallel
+    # access
+    
+    with scheduler.lock:
+        with log_file.open("a") as f:
+            f.write(json.dumps(
+                {
+                    'user_input': user_input,
+                    'retrieved_context': context_for_query,
+                    'model_response': prediction
+                }
+            ))
+            f.write("\n")
+    
+    return prediction
+
+
+textbox = gr.Textbox(placeholder="Enter your query here", lines=6)
+
+# Create the interface
+demo = gr.Interface(
+    inputs=textbox, fn=predict, outputs="text",
+    title="AMA on Tesla 10-K statements",
+    description="This web API presents an interface to ask questions on contents of the Tesla 10-K reports for the period 2019 - 2023.",
+    article="Note that questions that are not relevant to the Tesla 10-K report will not be answered.",
+    examples=[["What was the total revenue of the company in 2022?", "$ 81.46 Billion"],
+              ["Summarize the Management Discussion and Analysis section of the 2021 report in 50 words.", ""],
+              ["What was the company's debt level in 2020?", ""],
+              ["Identify 5 key risks identified in the 2019 10k report? Respond with bullet point summaries.", ""]
+             ],
+    concurrency_limit=16
+)
+
+demo.queue()
+demo.launch()

requirements.txt

@@ -1 +1,5 @@
-huggingface_hub==0.22.2
+openai==1.23.2
+chromadb==0.4.22
+langchain==0.1.9
+langchain-community==0.0.32
+sentence-transformers==2.3.1

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