Convert Entity Relationship Extraction in DSPy to using CoT

[NumberChiffre]

Description

DSPy has typed predictors/CoT that uses pydantic models as schema for their return types, however this is not as reliable as non-typed like CoT (because if something goes wrong with the formatting, you can catch it and resolve it yourself, often times some JSON parsing error or response from outputfield ends up in the prediction.rationale of CoT). Therefore, this PR is here to resolve these formatting issues with DSPy once and for all (hopefully) and run MIPROv2 to generate optimal prompt instructions for entity relationship extraction.

Misc:

• Need to resolve this before moving forward, or an alternative way: MIPROv2 crashes with AssertionError: No input variables found in the example stanfordnlp/dspy#1506
• Switched from DeepSeek Beta version with max output token of 8k instead of 4k.
• Qwen2-7B works with entity relationship extraction using DSPy, but needs further fine-tuning with more examples

[codecov]

Codecov Report

Attention: Patch coverage is 98.66667% with 1 line in your changes missing coverage. Please review.

Project coverage is 94.25%. Comparing base (f11e9f2) to head (20bb681).
Report is 12 commits behind head on main.

Files with missing lines	Patch %	Lines
nano_graphrag/entity_extraction/extract.py	96.77%	1 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main      #44      +/-   ##
==========================================
- Coverage   94.36%   94.25%   -0.12%     
==========================================
  Files          11       12       +1     
  Lines        1189     1288      +99     
==========================================
+ Hits         1122     1214      +92     
- Misses         67       74       +7     

Flag	Coverage Δ
	94.25% <98.66%> (?)

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