Add MODAIC token support and fix init to accept kwargs

README.md

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+Modaic and Weaviate
+Modaic is a hub to share and manage DSPy programs!
+
+You can learn more about the modaic sdk here and check out programs on the hub here.
+
+This notebook will illustrate how to load the CrossEncoderRanker program from the Modaic Hub, as well as PromptToSignature.
+
+Further, check out ce_ranker.py and pyproject.toml to see how to publish your programs on the Modaic Hub!
+
+CrossEncoderRanker
+from dotenv import load_dotenv
+import os
+from modaic import AutoProgram
+
+load_dotenv()
+
+# This is looking for MODAIC_TOKEN, WEAVIATE_URL, WEAVIATE_API_KEY, and OPENAI_API_KEY
+
+ce_ranker = AutoProgram.from_precompiled(
+    "connor/CrossEncoderRanker",
+    config_options={ # replace this with your collection name and other custom parameters
+        "collection_name": "IRPapersText_Default",
+        "return_properties": ["content"],
+        "k": 5
+    }
+)
+
+response = ce_ranker("What does HyDE stand for?");
+
+for idx, ranked_doc in enumerate(response):
+    print(f"Rank {idx+1}: {ranked_doc[:300]}")
+SSSS connor/CrossEncoderRanker
+Rank 1: Figure 1: An illustration of the HyDE model. Document snippets are shown. HyDE serves all types of queries without changing the underlying InstructGPT and Contriever/mContriever models.
+
+GPT-3 (Brown et al., 2020) models can be aligned to human intents to follow instructions faithfully.
+
+With these 
+Rank 2: |           | Scifact | FiQA  | DBPedia |
+|-----------|---------|-------|---------|
+| Contriever | 64.9    | 24.5  | 29.2    |
+| HyDE w/ InstructGPT | **69.1**  | 27.3  | 36.8    |
+| w/ GPT-3  | 65.9    | **27.9**  | **40.5**    |
+
+Table 5: nDCG@10 comparing InstructGPT vs. 3-shot GPT-3 on BEIR. Bes
+Rank 3: 6 Conclusion
+
+In this paper, we introduce HyDE, a new approach for building effective dense retrievers in a completely unsupervised manner, without the need for any relevance labels. We demonstrate that some aspects of relevance modeling can be delegated to a more powerful, flexible, and general-pur
+Rank 4: estimate Equation 5 by sampling N documents from g, [d̂1, d̂2, ..., d̂N]:
+
+v̂qij = 1/N ∑_(d̂k ∼ g(qij, INSTi)) f(d̂k)    (6)
+
+      = 1/N ∑_(k=1)^N f(d̂k)                        (7)
+
+We also consider the query as a possible hypothesis:
+
+v̂qij = 1/(N+1) [(∑_(k=1)^N f(d̂k)) + f(qij)]      (8)
+
+Inner p
+Rank 5: |           | sw  | wo  | ko   | ja   | bn   |
+|-----------|-----|-----|------|------|------|
+| **Unsupervised**         |     |     |      |      |      |
+| BM25      | 38.9| 28.5| 21.2 | 41.8 |      |
+| mContriever| 38.3| 22.3| 19.5 | 35.3 |      |
+| HyDE      | 41.7| 30.6| 30.7 | 41.3 |      |
+| 
+PromptToSignature
+from dotenv import load_dotenv
+import os
+from modaic import AutoProgram
+
+load_dotenv() # This is looking for MODAIC_TOKEN and OPENROUTER_API_KEY
+
+agent = AutoProgram.from_precompiled(
+    "fadeleke/prompt-to-signature",
+    config_options={
+        "lm": "openrouter/anthropic/claude-sonnet-4.5",
+        "max_tokens": 32000,
+        "temperature": 0.7,
+    }
+)
+
+result = agent("Summarize a document and extract key entities")
+
+print(result)
+SSSS fadeleke/prompt-to-signature
+Signature generation took 0.00 seconds in inference.
+Prediction(
+    signature_name='DocumentSummaryAndEntityExtraction',
+    task_description='Extract a concise summary of a document and identify key entities (people, organizations, locations, dates, etc.) mentioned within it.',
+    signature_fields=[GeneratedField(name='document', type=<FieldType.STRING: 'str'>, role=<FieldRole.INPUT: 'input'>, description='The document text to summarize and extract entities from', pydantic_model_schema=None, literal_values=None, default_value=None), GeneratedField(name='summary', type=<FieldType.STRING: 'str'>, role=<FieldRole.OUTPUT: 'output'>, description="A concise summary of the document's main points and content", pydantic_model_schema=None, literal_values=None, default_value=None), GeneratedField(name='key_entities', type=<FieldType.PYDANTIC_MODEL: 'pydantic'>, role=<FieldRole.OUTPUT: 'output'>, description='Structured extraction of key entities found in the document', pydantic_model_schema=PydanticModelSchema(model_name='KeyEntities', description='Container for extracted entities from the document', fields=[PydanticFieldDef(name='people', type=<FieldType.LIST_STRING: 'list[str]'>, description='Names of people mentioned in the document', required=False, literal_values=None, nested_model=None), PydanticFieldDef(name='organizations', type=<FieldType.LIST_STRING: 'list[str]'>, description='Names of organizations, companies, or institutions mentioned', required=False, literal_values=None, nested_model=None), PydanticFieldDef(name='locations', type=<FieldType.LIST_STRING: 'list[str]'>, description='Geographic locations, cities, countries, or regions mentioned', required=False, literal_values=None, nested_model=None), PydanticFieldDef(name='dates', type=<FieldType.LIST_STRING: 'list[str]'>, description='Important dates or time references mentioned in the document', required=False, literal_values=None, nested_model=None), PydanticFieldDef(name='topics', type=<FieldType.LIST_STRING: 'list[str]'>, description='Main topics or themes covered in the document', required=False, literal_values=None, nested_model=None)]), literal_values=None, default_value=None)],
+    reasoning=None
+)