diff --git a/application/.env_sample b/application/.env_sample new file mode 100644 index 00000000..3ad03dbe --- /dev/null +++ b/application/.env_sample @@ -0,0 +1 @@ +OPENAI_API_KEY=your_api_key \ No newline at end of file diff --git a/application/Dockerfile b/application/Dockerfile new file mode 100644 index 00000000..a8e778b5 --- /dev/null +++ b/application/Dockerfile @@ -0,0 +1,11 @@ +FROM python:3.9 + +WORKDIR /app +COPY . /app +RUN pip install --no-cache-dir -r requirements.txt +ENV FLASK_APP=app.py +ENV FLASK_ENV=development + +EXPOSE 5000 + +CMD ["flask", "run", "--host=0.0.0.0"] diff --git a/application/app.py b/application/app.py new file mode 100644 index 00000000..eeb00c34 --- /dev/null +++ b/application/app.py @@ -0,0 +1,61 @@ +import os +import pickle +import dotenv +import datetime +from flask import Flask, request, render_template +# os.environ["LANGCHAIN_HANDLER"] = "langchain" +import faiss +from langchain import OpenAI +from langchain.chains import VectorDBQAWithSourcesChain +from langchain.prompts import PromptTemplate + +# loading the .env file +dotenv.load_dotenv() + +# loading the index and the store and the prompt template +index = faiss.read_index("docs.index") +with open("combine_prompt.txt", "r") as f: + template = f.read() + +with open("faiss_store.pkl", "rb") as f: + store = pickle.load(f) + +app = Flask(__name__) + + +@app.route("/") +def home(): + return render_template("index.html") + + +@app.route("/api/answer", methods=["POST"]) +def api_answer(): + data = request.get_json() + question = data["question"] + + store.index = index + # create a prompt template + c_prompt = PromptTemplate(input_variables=["summaries", "question"], template=template) + # create a chain with the prompt template and the store + chain = VectorDBQAWithSourcesChain.from_llm(llm=OpenAI(temperature=0), vectorstore=store, combine_prompt=c_prompt) + # fetch the answer + result = chain({"question": question}) + + # some formatting for the frontend + result['answer'] = result['answer'].replace("\\n", "
") + result['answer'] = result['answer'].replace("SOURCES:", "") + + return result + + +# handling CORS +@app.after_request +def after_request(response): + response.headers.add('Access-Control-Allow-Origin', '*') + response.headers.add('Access-Control-Allow-Headers', 'Content-Type,Authorization') + response.headers.add('Access-Control-Allow-Methods', 'GET,PUT,POST,DELETE,OPTIONS') + return response + + +if __name__ == "__main__": + app.run(debug=True) diff --git a/application/combine_prompt.txt b/application/combine_prompt.txt new file mode 100644 index 00000000..ced95a82 --- /dev/null +++ b/application/combine_prompt.txt @@ -0,0 +1,27 @@ +You are a DocsGPT bot assistant by Arc53 that provides help with programming libraries. You give thorough answers with code examples. +Given the following extracted parts of a long document and a question, create a final answer with references ("SOURCES"). +ALWAYS return a "SOURCES" part in your answer. + +QUESTION: How to merge tables in pandas? +========= +Content: pandas provides various facilities for easily combining together Series or DataFrame with various kinds of set logic for the indexes and relational algebra functionality in the case of join / merge-type operations. +Source: 28-pl +Content: pandas provides a single function, merge(), as the entry point for all standard database join operations between DataFrame or named Series objects: \n\npandas.merge(left, right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False, validate=None) +Source: 30-pl +========= +FINAL ANSWER: To merge two tables in pandas, you can use the pd.merge() function. The basic syntax is: \n\npd.merge(left, right, on, how) \n\nwhere left and right are the two tables to merge, on is the column to merge on, and how is the type of merge to perform. \n\nFor example, to merge the two tables df1 and df2 on the column 'id', you can use: \n\npd.merge(df1, df2, on='id', how='inner') +SOURCES: 28-pl 30-pl + +QUESTION: How to eat vegetables using pandas? +========= +Content: ExtensionArray.repeat(repeats, axis=None) Returns a new ExtensionArray where each element of the current ExtensionArray is repeated consecutively a given number of times. \n\nParameters: repeats int or array of ints. The number of repetitions for each element. This should be a positive integer. Repeating 0 times will return an empty array. axis (0 or ‘index’, 1 or ‘columns’), default 0 The axis along which to repeat values. Currently only axis=0 is supported. +Source: 0-pl +========= +FINAL ANSWER: You can't eat vegetables using pandas. You can only eat them using your mouth. +SOURCES: + +QUESTION: {question} +========= +{summaries} +========= +FINAL ANSWER: \ No newline at end of file diff --git a/application/docs.index b/application/docs.index new file mode 100644 index 00000000..b5e33008 Binary files /dev/null and b/application/docs.index differ diff --git a/application/faiss_store.pkl b/application/faiss_store.pkl new file mode 100644 index 00000000..bef85b3e Binary files /dev/null and b/application/faiss_store.pkl differ diff --git a/application/package-lock.json b/application/package-lock.json new file mode 100644 index 00000000..7f24e071 --- /dev/null +++ b/application/package-lock.json @@ -0,0 +1,1331 @@ +{ + "name": "llmtests", + "lockfileVersion": 2, + "requires": true, + "packages": { + "": { + "devDependencies": { + "tailwindcss": "^3.2.4" + } + }, + "node_modules/@nodelib/fs.scandir": { + "version": "2.1.5", + "resolved": "https://registry.npmjs.org/@nodelib/fs.scandir/-/fs.scandir-2.1.5.tgz", + 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+python-dotenv==0.21.1 +PyYAML==6.0 +requests==2.28.2 +SQLAlchemy==1.4.46 +tqdm==4.64.1 +typing-inspect==0.8.0 +typing_extensions==4.4.0 +urllib3==1.26.14 +Werkzeug==2.2.2 +yarl==1.8.2 diff --git a/application/static/src/chat.js b/application/static/src/chat.js new file mode 100644 index 00000000..ee53617a --- /dev/null +++ b/application/static/src/chat.js @@ -0,0 +1,44 @@ +var el = document.getElementById('message-form'); +if (el) { + el.addEventListener("submit", function (event) { + console.log("submitting") + event.preventDefault() + var message = document.getElementById("message-input").value; + msg_html = '

' + msg_html += message + msg_html += '

' + document.getElementById("messages").innerHTML += msg_html; + let chatWindow = document.getElementById("chat-container"); + chatWindow.scrollTop = chatWindow.scrollHeight; + document.getElementById("message-input").value = ""; + document.getElementById("button-submit").innerHTML = ' Thinking...'; + document.getElementById("button-submit").disabled = true; + + fetch('/api/answer', { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify({question: message}), + }) + .then(response => response.json()) + .then(data => { + console.log('Success:', data); + msg_html = '
' + msg_html += data.answer + msg_html += '
' + document.getElementById("messages").innerHTML += msg_html; + let chatWindow = document.getElementById("chat-container"); + chatWindow.scrollTop = chatWindow.scrollHeight; + document.getElementById("button-submit").innerHTML = 'Send'; + document.getElementById("button-submit").disabled = false; + }) + .catch((error) => { + console.error('Error:', error); + document.getElementById("button-submit").innerHTML = 'Send'; + document.getElementById("button-submit").disabled = false; + }); + + + }); +} \ No newline at end of file diff --git a/application/static/src/input.css b/application/static/src/input.css new file mode 100644 index 00000000..8d6c99b5 --- /dev/null +++ b/application/static/src/input.css @@ -0,0 +1,56 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; + +#chat-container { + height: 44rem; + background-color: white; + padding: 10px; + overflow: auto; +} + + + +.bg-gray-200 { + background-color: #edf2f7; + } + + .bg-gray-900 { + background-color: #1a202c; + } + + .rounded-lg { + border-radius: 0.5rem; + } + + .shadow { + box-shadow: 0 1px 3px rgba(0, 0, 0, 0.12), 0 1px 2px rgba(0, 0, 0, 0.24); + } + + .text-gray-700 { + color: #4a5568; + } + + .text-sm { + font-size: 0.875rem; + } + + .p-4 { + padding: 1.5rem; + } + +.loader { + border: 16px solid #f3f3f3; /* Light grey */ + border-top: 16px solid #3498db; /* Blue */ + border-radius: 50%; + width: 120px; + height: 120px; + animation: spin 2s linear infinite; +} + +@keyframes spin { + 0% { transform: rotate(0deg); } + 100% { transform: rotate(360deg); } +} + + diff --git a/application/tailwind.config.js b/application/tailwind.config.js new file mode 100644 index 00000000..b91b5570 --- /dev/null +++ b/application/tailwind.config.js @@ -0,0 +1,8 @@ +/** @type {import('tailwindcss').Config} */ +module.exports = { + content: ["./templates/**/*.html", "./static/src/**/*.js"], + theme: { + extend: {}, + }, + plugins: [], +} diff --git a/application/templates/index.html b/application/templates/index.html new file mode 100644 index 00000000..2a25249b --- /dev/null +++ b/application/templates/index.html @@ -0,0 +1,65 @@ + + + + DocsGPT 🦖 Preview + + + + + + +
+

DocsGPT

+ About +
+
+
+
+
+ +
+
+

Hello, ask me anything about this library. Im here to help

+
+
+

How to create API key for Api gateway?

+
+
+

Import the boto3 library and create a client for the API Gateway service:

+ + +
+
+ client = boto3.client('apigateway') + + +
+
+

Create an API key:

+ +
+
+ response = client.create_api_key(
name='API_KEY_NAME',
description='API key description',
enabled=True)
api_key = response['value']
+ +
+
+
+ +
+
+ + +
+
+ + +
+
+
+

This is a chatbot that uses the GPT-3, Faiss and Longchain to answer questions

+

The source code is available on Github

+
+
+ + + diff --git a/scripts/ingest_rst.py b/scripts/ingest_rst.py new file mode 100644 index 00000000..03d090e8 --- /dev/null +++ b/scripts/ingest_rst.py @@ -0,0 +1,40 @@ +from pathlib import Path +from langchain.text_splitter import CharacterTextSplitter +import faiss +from langchain.vectorstores import FAISS +from langchain.embeddings import OpenAIEmbeddings +import pickle +import dotenv +import os + +dotenv.load_dotenv() + + +# Here we load in the data in the format that Notion exports it in. +ps = list(Path("pandasdocs/").glob("**/*.rst")) +# parse all child directories + +data = [] +sources = [] +for p in ps: + with open(p) as f: + data.append(f.read()) + sources.append(p) + +# Here we split the documents, as needed, into smaller chunks. +# We do this due to the context limits of the LLMs. +text_splitter = CharacterTextSplitter(chunk_size=1500, separator="\n") +docs = [] +metadatas = [] +for i, d in enumerate(data): + splits = text_splitter.split_text(d) + docs.extend(splits) + metadatas.extend([{"source": sources[i]}] * len(splits)) + + +# Here we create a vector store from the documents and save it to disk. +store = FAISS.from_texts(docs, OpenAIEmbeddings(), metadatas=metadatas) +faiss.write_index(store.index, "docs.index") +store.index = None +with open("faiss_store.pkl", "wb") as f: + pickle.dump(store, f)