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ed08773de7
Closes https://github.com/Unstructured-IO/unstructured/issues/1414
Closes #2039
This PR:
- Uses Pinecone python cli to implement a destination connector for
Pinecone and provides the ingest readme requirements
[(here)](https://github.com/Unstructured-IO/unstructured/tree/main/unstructured/ingest#the-checklist)
for the connector
- Updates documentation for the s3 destination connector
- Alphabetically sorts setup.py contents
- Updates logs for the chunking node in ingest pipeline
- Adds a baseline session handle implementation for destination
connectors, to be able to parallelize their operations
- For the
[bug](https://github.com/Unstructured-IO/unstructured/issues/1892)
related to persisting element data to ingest embedding nodes; this PR
tests the
[solution](https://github.com/Unstructured-IO/unstructured/pull/1893)
with its ingest test
- Solves a bug on ingest chunking params with [bugfix on chunking params
and implementing related
test](69e1949a6f)
---------
Co-authored-by: Roman Isecke <136338424+rbiseck3@users.noreply.github.com>
16 lines
1017 B
ReStructuredText
16 lines
1017 B
ReStructuredText
Embedding Configuration
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=========================
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A common embedding configuration is a critical component that allows for dynamic selection of embedders and
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their associated parameters to create vectors from data. This configuration provides the flexibility to choose
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from various embedding models and fine-tune parameters to optimize the quality and characteristics of the resulting vectors. It
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enables users to tailor the embedding process to the specific needs of their data and downstream applications,
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ensuring that the generated vectors effectively capture semantic relationships and contextual information within
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the dataset.
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Configs
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---------------------
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* ``embedding_provider``: An unstructured embedding provider to use while doing embedding. A few examples: langchain-openai, langchain-huggingface, langchain-aws-bedrock.
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* ``embedding_api_key``: If an api key is required to generate the embeddings via an api (i.e. OpenAI)
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* ``embedding_model_name``: The model to use for the embedder, if necessary.
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