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Update tech_stuff.py
Browse files- tech_stuff.py +14 -10
tech_stuff.py
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@@ -2,37 +2,41 @@ import os
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api_key = os.environ.get("api_key")
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prefix = "Following the Entrepreneur First Edge compatibility and intersection approach, with the potential of building a billion
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how_does_it_work = """
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This app helps you find a potential co-founder using AI and LLMs.
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#### Co-finder:
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The main component is the OpenAI embedding retrieval model that
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Link to documentation: https://platform.openai.com/docs/guides/embeddings
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Because this is a
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Example:
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Profile A is an expert in biotech and is looking for someone business
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The new augmented version of this profile will be
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Prefix + Profile description
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The intuition is to steer the retrieval to make sure it
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You can change the prefix directly in this page.
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Note, the finder works way better using ColbertV2 embeddings but it was a nightmare to deploy on huggingface, and since it is more of an experiment and either way nothing will replace human interactions, openai embeddings should be enough for now.
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### Simulator:
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This is simply a call to GPT4 with the two
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"""
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@@ -44,7 +48,7 @@ You are an expert in assessing startup co-founding teams and finding their poten
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- ### How those Edges might intersect
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<concise bullet points>
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- ### What kind of potential ideas/industries should be discussed and why they might be hair
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< minimum 10 concise bullet points>
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- ### What common belief could be leveraged
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api_key = os.environ.get("api_key")
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#prefix = "Following the Entrepreneur First Edge compatibility and intersection approach, with the potential of building a billion-dollar company. \n\nMINIMUM POSSIBLE SKILL AND PROFILE OVERLAP! HIGHLY COMPLEMENTARY PROFILES TO HELP THIS PERSON CO-FOUND A BILLION-DOLLARS COMPANY\n"
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prefix = ""
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how_does_it_work = """
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This app helps you find a potential co-founder using AI and LLMs.
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#### Co-finder:
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The main component is the OpenAI embedding retrieval model that finds similarities based on the information given in the cohort's Dashboard
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Link to documentation: https://platform.openai.com/docs/guides/embeddings
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Because this is a similar engine and we are looking for complementary profiles, I considered two approaches.
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The first one consists of asking GPT4 to build interesting complementary characteristics, which is the implemented one now.
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The second one consisted of adding a small prefix before the profile we want to find a match for.
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Example:
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Profile A is an expert in biotech and is looking for someone business-oriented
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The new augmented version of this profile will be
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Prefix + Profile description
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The intuition is to steer the retrieval to make sure it considers complementary profiles as being the most similar to the augmented profile version.
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Note, the finder works way better using ColbertV2 embeddings but it was a nightmare to deploy on huggingface, and since it is more of an experiment and either way nothing will replace human interactions, openai embeddings should be enough for now.
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### Simulator:
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This is simply a call to GPT4 with the two profile descriptions. You can adapt the system prompt here too.
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"""
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- ### How those Edges might intersect
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<concise bullet points>
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- ### What kind of potential ideas/industries should be discussed and why they might be hair-on-fire problems
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< minimum 10 concise bullet points>
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- ### What common belief could be leveraged
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