8ecb5d3a0c
* Add documentation code validation system - Create runnable example scripts in examples/api/ (19 files) - Add CodeSnippet component for extracting marked sections - Add raw-loader dependency for importing source files - Create sample retain-new.mdx showing new approach - Add README documenting coverage and gaps * Fix wheel glob expansion in test-doc-examples CI job * Fix CI issue * Fix wheel path - uv build outputs to repo root dist/ * Fix: use explicit shell expansion for wheel install * Fix: run cd in subshell so install runs from repo root * Add documentation code validation CI job - Use uv sync + uv run pattern (matches existing CI) - Add requests to test dependencies for cleanup scripts * Fix async API client usage in documents.py example * Fix main-methods.py: RecallResult and ReflectFact don't have weight attribute * Fix opinions.py: use actual API attributes instead of non-existent ones * Fix example scripts: remove non-existent API attributes - recall.py: remove .weight, fix entities iteration (dict not list) - retain.mjs: remove result.async check
125 lines
3.4 KiB
Python
125 lines
3.4 KiB
Python
#!/usr/bin/env python3
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"""
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Main Methods overview examples for Hindsight.
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Run: python examples/api/main-methods.py
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"""
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import os
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import requests
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HINDSIGHT_URL = os.getenv("HINDSIGHT_API_URL", "http://localhost:8888")
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# =============================================================================
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# Setup (not shown in docs)
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# =============================================================================
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from hindsight_client import Hindsight
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client = Hindsight(base_url=HINDSIGHT_URL)
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# =============================================================================
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# Doc Examples - Retain Section
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# =============================================================================
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# [docs:main-retain]
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# Store a single fact
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client.retain(
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bank_id="my-bank",
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content="Alice joined Google in March 2024 as a Senior ML Engineer"
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)
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# Store a conversation
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conversation = """
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User: What did you work on today?
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Assistant: I reviewed the new ML pipeline architecture.
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User: How did it look?
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Assistant: Promising, but needs better error handling.
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"""
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client.retain(
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bank_id="my-bank",
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content=conversation,
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context="Daily standup conversation"
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)
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# Batch retain multiple items
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client.retain_batch(
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bank_id="my-bank",
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items=[
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{"content": "Bob prefers Python for data science"},
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{"content": "Alice recommends using pytest for testing"},
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{"content": "The team uses GitHub for code reviews"}
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]
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)
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# [/docs:main-retain]
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# =============================================================================
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# Doc Examples - Recall Section
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# =============================================================================
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# [docs:main-recall]
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# Basic search
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results = client.recall(
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bank_id="my-bank",
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query="What does Alice do at Google?"
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)
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for result in results.results:
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print(f"- {result.text}")
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# Search with options
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results = client.recall(
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bank_id="my-bank",
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query="What happened last spring?",
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budget="high", # More thorough graph traversal
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max_tokens=8192, # Return more context
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types=["world"] # Only world facts
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)
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# Include entity information
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results = client.recall(
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bank_id="my-bank",
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query="Tell me about Alice",
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include_entities=True,
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max_entity_tokens=500
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)
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# Check entity details
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for entity in results.entities or []:
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print(f"Entity: {entity.name}")
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print(f"Observations: {entity.observations}")
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# [/docs:main-recall]
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# =============================================================================
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# Doc Examples - Reflect Section
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# =============================================================================
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# [docs:main-reflect]
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# Basic reflect
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response = client.reflect(
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bank_id="my-bank",
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query="Should we adopt TypeScript for our backend?"
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)
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print(response.text)
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print("\nBased on:", len(response.based_on or []), "facts")
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# Reflect with options
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response = client.reflect(
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bank_id="my-bank",
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query="What are Alice's strengths for the team lead role?",
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budget="high" # More thorough reasoning
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)
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# See which facts influenced the response
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for fact in response.based_on or []:
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print(f"- {fact.text}")
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# [/docs:main-reflect]
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# =============================================================================
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# Cleanup (not shown in docs)
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# =============================================================================
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requests.delete(f"{HINDSIGHT_URL}/v1/default/banks/my-bank")
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print("main-methods.py: All examples passed")
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