ljnmedium

	API access solution - 3rd party model.	On-premise solution - open source model.
R&D developpement	The low initial cost, both in terms of time and money, allows us to quickly reach a Minimum Viable Product (MVP). The procedure for model parameter optimization and MLops is overseen by a third-party e

ljnmedium

Provider	Model	Cost for input	Cost for output	Cost per request.
OpenAI	text-davinci-004	$0.03/ 1K tokens	$0.06/ 1K tokens	0
OpenAI	text-davinci-003	$0.02/ 1K tokens	$0.02/ 1K tokens	0
OpenAI	text-davinci-002	$0.002/ 1K tokens	$0.002/ 1K tokens	0
OpenAI	gpt-3.5-turbo	$0.002/ 1K tokens	$0.002/ 1K tokens	0
[Cohere](https://cohere.com/pri

ljnmedium

Provider	Model	Number of parameters
Meta with Microsoft	LLama 2	7B, 13B, 32B, 65.2B
Meta	LLama	7B, 13B, 70B
Technology Innovation Institute of UAE	Flacon LLM	7B, 40B
Stanford’s CRFM	Alpaca	7B
Google	Plan-T5	80M, 250M, 780M, 3B, 11B
MPT	MosaicML	7B, 30B

ljnmedium

	Direct query Information appearing in text (entity extraction, summarization, find relevant paragraphs, etc … ).	Indirect query Inferenced information (mathematical calculation, comparison, conclusion, etc …).
Simple text Text containing descriptions excluding table.	Complexity: + Accuracy: +++	Complexity: ++ Accuracy: +++
Complex textText

ljnmedium

pinecone.init(api_key="YOUR_API_KEY", environment="YOUR_ENVIRONMENT")
index = pinecone.Index("projet_esg")

ljnmedium

index.describe_index_stats()

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values = embedd_model.encode([b['content'] for b in batch])
sparse_values = sparsed_model.encode([b['content'] for b in batch])

# Create unique IDs
ids = [str(b['metadata']['id']) for b in batch]

# Add all to upsert list
to_upsert = [{'id': i, 'values': v, 'metadata':m , 'sparse_values': sv} for (i,v,m,sv) in zip(ids,values, metas, sparse_values)]

# Upsert/insert these records to pinecone

ljnmedium

index.describe_index_stats()

# Return for example: 
 {
  'dimension': 1536,
  'index_fullness': 0.1,
  'namespaces': {
    'Eiffage': {'vector_count': 344},
    'total_vector_count': 344
  }

ljnmedium

from openai import Embedding
from pinecone_text.sparse import BM25Encoder

EMBEDDING_MODEL = "text-embedding-ada-002"
SPARSE_MODEL_FILE_NAME = "bm25_values.json"

class Embedding_Model(Embedding): 
  
    def __init__(self, model_name): 
        self.engine = model_name

ljnmedium

from pinecone import Index


class Retreiver(Index):
  
    def __init__(self, index_name, embedd_openai_model, sparse_model_file_name, dimension=1536, metric="dotproduct"): 
        self.index_name = index_name
        
        # Create index
        if index_name not in pinecone.list_indexes():