| import os |
|
|
| import plotly.express as px |
| import gradio as gr |
| import pandas as pd |
| from collections import defaultdict |
|
|
|
|
| from time import sleep |
| import openai |
|
|
| import json |
|
|
| import logging |
| from dotenv import load_dotenv |
| import pinecone |
|
|
| default_region = 'us-west1-gcp' |
| openai_embed_model = "text-embedding-ada-002" |
|
|
| def get_data(num_names_per_category=2, num_categories=3): |
|
|
| categories = ['Text', 'Bio', 'Audio', 'Legal', 'Chatbot/Conversational AI', 'Summarization', 'MLOps/Platform', 'Code', 'Avatars', 'Image', 'Semantic Search', 'Game', 'Video', 'Data', 'Sentiment Analysis'] |
| num_categories = min(num_categories, len((categories))) |
|
|
| data = query_pinecone(categories, num_categories, num_names_per_category) |
|
|
| return data |
|
|
| def get_openai_embedding(apikey, data): |
| """ Fetch an embedding w/ given data """ |
| openai.api_key = apikey |
| try: |
| res = openai.Embedding.create(input=data, engine=openai_embed_model) |
| except: |
| done = False |
| while not done: |
| sleep(5) |
| try: |
| res = openai.Embedding.create( |
| input=data, engine=openai_embed_model) |
| print('retrying') |
| done = True |
| except Exception as e: |
| logging.exception(e) |
| return res |
|
|
| def query_pinecone(categories, n_categories=3, top_k=4): |
| load_dotenv() |
|
|
| pinecone.init( |
| api_key=os.getenv('PINECONE_API_KEY'), |
| environment=os.getenv('PINECONE_ENVIRONMENT'), |
| ) |
| index = pinecone.Index("vc-content-oracle-big") |
|
|
| out = defaultdict(list) |
|
|
| companies = set() |
|
|
| |
| for category in categories[:n_categories]: |
|
|
| |
| vector = get_openai_embedding(os.getenv('OPENAI_API_KEY'), category)['data'][0]['embedding'] |
|
|
| res = index.query( |
| vector=vector, |
| top_k=top_k, |
| include_metadata=True, |
| include_values=False, |
| ) |
|
|
| for match in res['matches']: |
| if 'Organization Name' not in match['metadata'] or 'Website' not in match['metadata']: |
| print('no Organization Name') |
| continue |
|
|
| |
| company_link = '<a href="' + match['metadata']['Website'] + '" style="cursor: pointer; color:white; font-color:white " target="_blank" rel="noopener noreferrer">' + match['metadata']['Organization Name'] + '</a>' |
|
|
| print(out[category], company_link) |
|
|
| contains = False |
| if match['metadata']['Organization Name'] in companies: |
| contains = True |
|
|
| if not contains: |
| out[category].append(company_link) |
| else: |
| out[category].append(None) |
| |
| companies.add(match['metadata']['Organization Name']) |
|
|
| return out |
|
|
| """ |
| returns { 'matches': [{ |
| 'id': 'A', |
| 'metadata': { |
| 'Organization Name': '', |
| 'Organization Name URL': '', |
| 'Last Funding Amount': '', |
| 'Last Funding Amount Currency': '', |
| 'Last Funding Amount Currency (in USD)': '', |
| 'Website': '', |
| 'Total Funding Amount': '', |
| 'Total Funding Amount Currency': '', |
| 'Total Funding Amount Currency (in USD)': '', |
| 'Last Funding Date': '', |
| 'Founded Date': '', |
| 'Founded Date Precision': '', |
| 'Last Funding Type': '', |
| 'Number of Employees': '', |
| 'Full Description': '', |
| 'Top 5 Investors': '', |
| }, |
| 'score': 0.01, |
| 'values':[] |
| }]} |
| """ |
|
|
| def make_plot(category, num_categories, num_names_per_category): |
|
|
| num_categories = int(num_categories) |
| num_names_per_category = int(num_names_per_category) |
|
|
| data = get_data(num_names_per_category, num_categories) |
| names = [] |
| parents = [] |
|
|
| for category, names_list in data.items(): |
| names.extend(names_list) |
| parents.extend([category] * len(names_list)) |
|
|
| unique_parents = list(set(parents)) |
| names += unique_parents |
| parents += [''] * len(unique_parents) |
|
|
| fig = px.treemap( |
| names=names, |
| parents=parents, |
|
|
| ) |
|
|
| fig.update_traces(root_color="lightgrey") |
| fig.update_traces(pathbar_textfont_color="white") |
| fig.update_layout(margin = dict(t=50, l=25, r=25, b=25)) |
| return fig |
|
|
| def add_text(state, text): |
|
|
| |
|
|
| load_dotenv() |
|
|
| pinecone.init( |
| api_key=os.getenv('PINECONE_API_KEY'), |
| environment=os.getenv('PINECONE_ENVIRONMENT'), |
| ) |
| index = pinecone.Index("vc-content-oracle-big") |
|
|
| vector = get_openai_embedding(os.getenv('OPENAI_API_KEY'), text)['data'][0]['embedding'] |
|
|
| |
|
|
| res = index.query( |
| vector=vector, |
| top_k=3, |
| include_metadata=True, |
| include_values=False, |
| ) |
|
|
| context = 'You are Andressen Horowitz, a Venture Capitalist. You know everything about startup investment, particularly in Generative AI. Everytime you are asked a question respond faithfully and consistently. Answer the question based on the context below \n\n' |
|
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| |
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| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
|
|
| for match in res['matches'][:1]: |
| context += ( |
| 'Context\n: Name: ' + |
| str(match['metadata'].get('Organization Name', '')) + |
| 'Funding: ' + |
| str(match['metadata'].get('Total Funding Amount', '')) + |
| 'Founding Date: ' + |
| str(match['metadata'].get('Founded Date', '')) + |
| 'Number of Employees: ' + |
| str(match['metadata'].get('Number of Employees', '')) + |
| 'Description: ' + |
| str(match['metadata'].get('Full Description', '')) + |
| 'Investors: ' + |
| str(match['metadata'].get('Top 5 Investors', '')) |
| ) |
|
|
| context += 'Question: ' + text + '\nAnswer:' |
|
|
| response = openai.Completion.create( |
| model="text-davinci-003", |
| prompt=context, |
| max_tokens=200, |
| temperature=0, |
| ) |
|
|
| state = state + [(text, response['choices'][0]['text'])] |
|
|
| return state, state |
|
|
| with gr.Blocks() as demo: |
|
|
| |
| title = gr.HTML('<h1 style="text-align: center; color: white; font-size: 50px; font-family: Helvetica">ChatGVC</h1>') |
|
|
| |
| subtitle = gr.HTML('<h2 style="text-align: center; color: white; font-size: 20px; font-family: Helvetica">Auto-Generated VC Market Maps</h2>') |
|
|
| category = gr.Textbox(label='Market Category') |
| n_subcategories = gr.Number(label="Number of Subcategories") |
| n_companies = gr.Number(label="Max Number of Companies per Subcategory") |
| button = gr.Button("Generate") |
| plot = gr.Plot(label="Plot") |
| button.click(make_plot, inputs=[category, n_subcategories, n_companies], outputs=[plot]) |
| |
|
|
| chatbot = gr.Chatbot(elem_id="chatbot") |
| state = gr.State([]) |
| |
| with gr.Row(): |
| with gr.Column(scale=0.85): |
| txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter, or upload an image").style(container=False) |
| |
| txt.submit(add_text, [state, txt], [state, chatbot]) |
| txt.submit(lambda :"", None, txt) |
|
|
| demo.launch(share=True) |
|
|
