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+FROM python:3.8-slim-buster as langchain-serve-img
+
+RUN pip3 install langchain-serve
+RUN pip3 install api
+
+CMD [ "lc-serve", "deploy", "local", "api" ]
+
+FROM python:3.8-slim-buster as pdf-gpt-img
+
+WORKDIR /app
+
+COPY requirements.txt requirements.txt
+RUN pip3 install -r requirements.txt
+
+COPY . .
+
+CMD [ "python3", "app.py" ]

api.py

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+import os
+import re
+import shutil
+import urllib.request
+from pathlib import Path
+from tempfile import NamedTemporaryFile
+
+import fitz
+import numpy as np
+import openai
+import tensorflow_hub as hub
+from fastapi import UploadFile
+from lcserve import serving
+from sklearn.neighbors import NearestNeighbors
+
+
+recommender = None
+
+
+def download_pdf(url, output_path):
+    urllib.request.urlretrieve(url, output_path)
+
+
+def preprocess(text):
+    text = text.replace('\n', ' ')
+    text = re.sub('\s+', ' ', text)
+    return text
+
+
+def pdf_to_text(path, start_page=1, end_page=None):
+    doc = fitz.open(path)
+    total_pages = doc.page_count
+
+    if end_page is None:
+        end_page = total_pages
+
+    text_list = []
+
+    for i in range(start_page - 1, end_page):
+        text = doc.load_page(i).get_text("text")
+        text = preprocess(text)
+        text_list.append(text)
+
+    doc.close()
+    return text_list
+
+
+def text_to_chunks(texts, word_length=150, start_page=1):
+    text_toks = [t.split(' ') for t in texts]
+    chunks = []
+
+    for idx, words in enumerate(text_toks):
+        for i in range(0, len(words), word_length):
+            chunk = words[i : i + word_length]
+            if (
+                (i + word_length) > len(words)
+                and (len(chunk) < word_length)
+                and (len(text_toks) != (idx + 1))
+            ):
+                text_toks[idx + 1] = chunk + text_toks[idx + 1]
+                continue
+            chunk = ' '.join(chunk).strip()
+            chunk = f'[Page no. {idx+start_page}]' + ' ' + '"' + chunk + '"'
+            chunks.append(chunk)
+    return chunks
+
+
+class SemanticSearch:
+    def __init__(self):
+        self.use = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')
+        self.fitted = False
+
+    def fit(self, data, batch=1000, n_neighbors=5):
+        self.data = data
+        self.embeddings = self.get_text_embedding(data, batch=batch)
+        n_neighbors = min(n_neighbors, len(self.embeddings))
+        self.nn = NearestNeighbors(n_neighbors=n_neighbors)
+        self.nn.fit(self.embeddings)
+        self.fitted = True
+
+    def __call__(self, text, return_data=True):
+        inp_emb = self.use([text])
+        neighbors = self.nn.kneighbors(inp_emb, return_distance=False)[0]
+
+        if return_data:
+            return [self.data[i] for i in neighbors]
+        else:
+            return neighbors
+
+    def get_text_embedding(self, texts, batch=1000):
+        embeddings = []
+        for i in range(0, len(texts), batch):
+            text_batch = texts[i : (i + batch)]
+            emb_batch = self.use(text_batch)
+            embeddings.append(emb_batch)
+        embeddings = np.vstack(embeddings)
+        return embeddings
+
+
+def load_recommender(path, start_page=1):
+    global recommender
+    if recommender is None:
+        recommender = SemanticSearch()
+
+    texts = pdf_to_text(path, start_page=start_page)
+    chunks = text_to_chunks(texts, start_page=start_page)
+    recommender.fit(chunks)
+    return 'Corpus Loaded.'
+
+
+def generate_text(openAI_key, prompt, engine="text-davinci-003"):
+    openai.api_key = openAI_key
+    try:
+        completions = openai.Completion.create(
+            engine=engine,
+            prompt=prompt,
+            max_tokens=512,
+            n=1,
+            stop=None,
+            temperature=0.7,
+        )
+        message = completions.choices[0].text
+    except Exception as e:
+        message = f'API Error: {str(e)}'
+    return message 
+
+
+def generate_answer(question, openAI_key):
+    topn_chunks = recommender(question)
+    prompt = ""
+    prompt += 'search results:\n\n'
+    for c in topn_chunks:
+        prompt += c + '\n\n'
+
+    prompt += (
+        "Instructions: Compose a comprehensive reply to the query using the search results given. "
+        "Cite each reference using [ Page Number] notation (every result has this number at the beginning). "
+        "Citation should be done at the end of each sentence. If the search results mention multiple subjects "
+        "with the same name, create separate answers for each. Only include information found in the results and "
+        "don't add any additional information. Make sure the answer is correct and don't output false content. "
+        "If the text does not relate to the query, simply state 'Text Not Found in PDF'. Ignore outlier "
+        "search results which has nothing to do with the question. Only answer what is asked. The "
+        "answer should be short and concise. Answer step-by-step. \n\nQuery: {question}\nAnswer: "
+    )
+
+    prompt += f"Query: {question}\nAnswer:"
+    answer = generate_text(openAI_key, prompt, "text-davinci-003")
+    return answer
+
+
+def load_openai_key() -> str:
+    key = os.environ.get("OPENAI_API_KEY")
+    if key is None:
+        raise ValueError(
+            "[ERROR]: Please pass your OPENAI_API_KEY. Get your key here : https://platform.openai.com/account/api-keys"
+        )
+    return key
+
+
+@serving
+def ask_url(url: str, question: str):
+    download_pdf(url, 'corpus.pdf')
+    load_recommender('corpus.pdf')
+    openAI_key = load_openai_key()
+    return generate_answer(question, openAI_key)
+
+
+@serving
+async def ask_file(file: UploadFile, question: str) -> str:
+    suffix = Path(file.filename).suffix
+    with NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
+        shutil.copyfileobj(file.file, tmp)
+        tmp_path = Path(tmp.name)
+
+    load_recommender(str(tmp_path))
+    openAI_key = load_openai_key()
+    return generate_answer(question, openAI_key)

app.py

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+import json
+from tempfile import _TemporaryFileWrapper
+
+import gradio as gr
+import requests
+
+
+def ask_api(
+    lcserve_host: str,
+    url: str,
+    file: _TemporaryFileWrapper,
+    question: str,
+    openAI_key: str,
+) -> str:
+    if not lcserve_host.startswith('http'):
+        return '[ERROR]: Invalid API Host'
+
+    if url.strip() == '' and file == None:
+        return '[ERROR]: Both URL and PDF is empty. Provide at least one.'
+
+    if url.strip() != '' and file != None:
+        return '[ERROR]: Both URL and PDF is provided. Please provide only one (either URL or PDF).'
+
+    if question.strip() == '':
+        return '[ERROR]: Question field is empty'
+
+    _data = {
+        'question': question,
+        'envs': {
+            'OPENAI_API_KEY': openAI_key,
+        },
+    }
+
+    if url.strip() != '':
+        r = requests.post(
+            f'{lcserve_host}/ask_url',
+            json={'url': url, **_data},
+        )
+
+    else:
+        with open(file.name, 'rb') as f:
+            r = requests.post(
+                f'{lcserve_host}/ask_file',
+                params={'input_data': json.dumps(_data)},
+                files={'file': f},
+            )
+
+    if r.status_code != 200:
+        raise ValueError(f'[ERROR]: {r.text}')
+
+    return r.json()['result']
+
+
+title = 'PDF GPT'
+description = """ PDF GPT allows you to chat with your PDF file using Universal Sentence Encoder and Open AI. It gives hallucination free response than other tools as the embeddings are better than OpenAI. The returned response can even cite the page number in square brackets([]) where the information is located, adding credibility to the responses and helping to locate pertinent information quickly."""
+
+with gr.Blocks() as demo:
+    gr.Markdown(f'<center><h1>{title}</h1></center>')
+    gr.Markdown(description)
+
+    with gr.Row():
+        with gr.Group():
+            lcserve_host = gr.Textbox(
+                label='Enter your API Host here',
+                value='http://localhost:8080',
+                placeholder='http://localhost:8080',
+            )
+            gr.Markdown(
+                '<p style="text-align:center">Get your Open AI API key <a href="https://platform.openai.com/account/api-keys">here</a></p>'
+            )
+            openAI_key = gr.Textbox(
+                label='Enter your OpenAI API key here', type='password'
+            )
+            pdf_url = gr.Textbox(label='Enter PDF URL here')
+            gr.Markdown("<center><h4>OR<h4></center>")
+            file = gr.File(
+                label='Upload your PDF/ Research Paper / Book here', file_types=['.pdf']
+            )
+            question = gr.Textbox(label='Enter your question here')
+            btn = gr.Button(value='Submit')
+            btn.style(full_width=True)
+
+        with gr.Group():
+            answer = gr.Textbox(label='The answer to your question is :')
+
+        btn.click(
+            ask_api,
+            inputs=[lcserve_host, pdf_url, file, question, openAI_key],
+            outputs=[answer],
+        )
+
+demo.app.server.timeout = 60000 # Set the maximum return time for the results of accessing the upstream server
+
+demo.launch(server_port=7860, enable_queue=True) # `enable_queue=True` to ensure the validity of multi-user requests

docker-compose.yaml

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+version: '3'
+
+services:
+  langchain-serve:
+    build:
+      context: . 
+      target: langchain-serve-img
+    ports:
+      - '8080:8080'
+  pdf-gpt:
+    build:
+      context: . 
+      target: pdf-gpt-img
+    ports:
+      - '7860:7860'

requirements.txt

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+PyMuPDF==1.22.1
+numpy==1.23.5
+scikit-learn==1.2.2
+tensorflow>=2.0.0
+tensorflow_hub==0.13.0
+openai==0.27.4
+gradio==3.34.0
+langchain-serve>=0.0.19