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NirantandAtita Arora
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* * feat(gemini.md): add documentation for integrating Gemini embeddings with Qdrant * * refactor(integrations): move cohere.md to embeddings folder * refactor(integrations): move openai.md to embeddings folder * refactor(integrations): move autogen.md to frameworks folder * refactor(integrations): move langchain.md to frameworks folder * blacken * * feat(embedding, frameworks): reorganise integrations into embedding and frameworks, add _index.md to both * * chore(gemini.md): remove old Gemini integration documentation * * chore(embedding/_index.md): update weight from 24 to 23 and set is_empty to false * chore(frameworks/_index.md): update weight from 24 to 23 and set is_empty to false * Split integrations into embedding and frameworks * Update heading level for embedding a document * Update Gemini embedding documentation * Update titles for embedding and frameworks sections * Try again with nesting * Add documentation for integrated frameworks and embedding options * Delete integrations documentation file * Add Delimiter; unknown weights * Change all weights to 3x * Delimiter reorg * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * * docs(embedding/gemini.md): update Gemini Embedding Model API documentation * * - Add information about the new Gemini Embedding Model and its compatibility with Qdrant * - Clarify the usage of the `task_type` parameter in the API call * - Provide a list of supported task types and * * docs(embedding): update list of embedding integrations * * refactor(fifty-one.md): Rename file from embedding/fifty-one.md to frameworks/fifty-one.md * refactor(txtai.md): Rename file from embedding/txtai.md to frameworks/txtai.md * * chore(embedding): update is_empty value to true in _index.md * chore(embedding): remove Fifty One from embedding/_index.md * embedding -> embeddings --------- Co-authored-by: Atita Arora <atarora@users.noreply.github.com>
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title, weight
| title | weight |
|---|---|
| ML6 Fondant | 1700 |
ML6 Fondant
Fondant is an open-source framework that aims to simplify and speed up large-scale data processing by making containerized components reusable across pipelines and execution environments.
Fondant features a Qdrant component image to load textual data and embeddings into a the database.
Usage
A data load pipeline for RAG using Qdrant.
from fondant.pipeline import ComponentOp, Pipeline
pipeline = Pipeline(
pipeline_name="ingestion-pipeline",
pipeline_description="Pipeline to prepare and process \
data for building a RAG solution",
base_path="./data-dir",
)
# An example data source component
load_from_source = ComponentOp(
component_dir="path/to/data-source-component",
arguments={
"n_rows_to_load": 10,
# Custom arguments for the component
},
)
chunk_text_op = ComponentOp.from_registry(
name="chunk_text",
arguments={
"chunk_size": 512,
"chunk_overlap": 32,
},
)
embed_text_op = ComponentOp.from_registry(
name="embed_text",
arguments={
"model_provider": "huggingface",
"model": "all-MiniLM-L6-v2",
},
)
# Getting the Qdrant component from the Fondant registry
index_qdrant_op = ComponentOp.from_registry(
name="index_qdrant",
arguments={
"url": "http:localhost:6333",
"collection_name": "some-collection-name",
},
)
# Construct your pipeline
pipeline.add_op(load_from_source)
pipeline.add_op(chunk_text_op, dependencies=load_from_source)
pipeline.add_op(embed_text_op, dependencies=chunk_text_op)
pipeline.add_op(index_qdrant_op, dependencies=embed_text_op)
Next steps
Find the FondantAI docs here.