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@@ -1,2 +1,8 @@
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OPENAI_API_KEY=<LLM api key (for example, open ai key)>
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OPENAI_API_KEY=<LLM api key (for example, open ai key)>
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EMBEDDINGS_KEY=<LLM embeddings api key (for example, open ai key)>
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EMBEDDINGS_KEY=<LLM embeddings api key (for example, open ai key)>
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# Azure
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OPENAI_API_BASE=
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OPENAI_API_VERSION=
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AZURE_DEPLOYMENT_NAME=
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AZURE_EMBEDDINGS_DEPLOYMENT_NAME=
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@@ -11,7 +11,7 @@ from retry import retry
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# from langchain.embeddings import CohereEmbeddings
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# from langchain.embeddings import CohereEmbeddings
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def num_tokens_from_string(string: str, encoding_name: str) -> int:
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def num_tokens_from_string(string: str, encoding_name: str) -> tuple[int, float]:
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# Function to convert string to tokens and estimate user cost.
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# Function to convert string to tokens and estimate user cost.
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encoding = tiktoken.get_encoding(encoding_name)
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encoding = tiktoken.get_encoding(encoding_name)
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num_tokens = len(encoding.encode(string))
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num_tokens = len(encoding.encode(string))
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@@ -45,7 +45,16 @@ def call_openai_api(docs, folder_name):
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# environment="us-east1-gcp" # next to api key in console
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# environment="us-east1-gcp" # next to api key in console
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# )
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# )
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# index_name = "pandas"
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# index_name = "pandas"
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store = FAISS.from_documents(docs_test, OpenAIEmbeddings())
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if ( # azure
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os.environ.get("OPENAI_API_BASE")
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and os.environ.get("OPENAI_API_VERSION")
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and os.environ.get("AZURE_DEPLOYMENT_NAME")
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):
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os.environ["OPENAI_API_TYPE"] = "azure"
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openai_embeddings = OpenAIEmbeddings(model=os.environ.get("AZURE_EMBEDDINGS_DEPLOYMENT_NAME"))
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else:
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openai_embeddings = OpenAIEmbeddings()
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store = FAISS.from_documents(docs_test, openai_embeddings)
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# store_pine = Pinecone.from_documents(docs_test, OpenAIEmbeddings(), index_name=index_name)
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# store_pine = Pinecone.from_documents(docs_test, OpenAIEmbeddings(), index_name=index_name)
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# Uncomment for MPNet embeddings
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# Uncomment for MPNet embeddings
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