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Update tech_stuff.py

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  1. tech_stuff.py +14 -10
tech_stuff.py CHANGED
@@ -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 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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-
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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 find 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 similarity engine and we are looking for complementary profiles we add a small prefix before the profile we want to match.
 
 
 
 
 
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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 consider complementary profiles as being the most similar to the augmented profile version.
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-
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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 profiles descriptions. You can adapt the system prompt here too.
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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 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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  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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+
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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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+
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+
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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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+
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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