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Boris Arzentar 2025-02-08 15:22:29 +01:00
commit 8b70359337
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8
README.md
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@ -14,12 +14,14 @@ We build for developers who need a reliable, production-ready data layer for AI
Cognee implements scalable, modular ECL (Extract, Cognify, Load) pipelines that allow you to interconnect and retrieve past conversations, documents, and audio transcriptions while reducing hallucinations, developer effort, and cost.
Cognee merges graph and vector databases to uncover hidden relationships and new patterns in your data. You can automatically model, load and retrieve entities and objects representing your business domain and analyze their relationships, uncovering insights that neither vector stores nor graph stores alone can provide. Learn more about use-cases [here](https://docs.cognee.ai/use_cases)
Cognee merges graph and vector databases to uncover hidden relationships and new patterns in your data. You can automatically model, load and retrieve entities and objects representing your business domain and analyze their relationships, uncovering insights that neither vector stores nor graph stores alone can provide. Learn more about use-cases [here](https://docs.cognee.ai/use_cases).
Try it in a Google Colab <a href="https://colab.research.google.com/drive/1g-Qnx6l_ecHZi0IOw23rg0qC4TYvEvWZ?usp=sharing">notebook</a> or have a look at our <a href="https://docs.cognee.ai">documentation</a>
Try it in a Google Colab <a href="https://colab.research.google.com/drive/1g-Qnx6l_ecHZi0IOw23rg0qC4TYvEvWZ?usp=sharing">notebook</a> or have a look at our <a href="https://docs.cognee.ai">documentation</a>.
If you have questions, join our <a href="https://discord.gg/NQPKmU5CCg">Discord</a> community
If you have questions, join our <a href="https://discord.gg/NQPKmU5CCg">Discord</a> community.
Have you seen cognee's <a href="https://github.com/topoteretes/cognee-starter">starter repo</a>? Check it out!
<div align="center">
<img src="assets/cognee_benefits.png" alt="why cognee" width="80%" />

1
cognee/tasks/storage/__init__.py
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@ -1,2 +1,3 @@
from .add_data_points import add_data_points
from .index_data_points import index_data_points
from .index_graph_edges import index_graph_edges

6
cognee/tasks/storage/index_data_points.py
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@ -33,8 +33,10 @@ async def index_data_points(data_points: list[DataPoint]):
indexed_data_point.metadata["index_fields"] = [field_name]
index_points[index_name].append(indexed_data_point)
for index_name, indexable_points in index_points.items():
index_name, field_name = index_name.split("_")
for index_name_and_field, indexable_points in index_points.items():
first_occurence = index_name_and_field.index("_")
index_name = index_name_and_field[:first_occurence]
field_name = index_name_and_field[first_occurence + 1:]
try:
await vector_engine.index_data_points(index_name, field_name, indexable_points)
except EmbeddingException as e: