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class="md-nav__item md-nav__item--pruned md-nav__item--nested"> <a href=../.. class=md-nav__link> <span class=md-ellipsis> Home </span> <span class="md-nav__icon md-icon"></span> </a> </li> <li class="md-nav__item md-nav__item--pruned md-nav__item--nested"> <a href=../../user-guide/installation/ class=md-nav__link> <span class=md-ellipsis> Documentation </span> <span class="md-nav__icon md-icon"></span> </a> </li> <li class="md-nav__item md-nav__item--pruned md-nav__item--nested"> <a href=../../architecture/overview/ class=md-nav__link> <span class=md-ellipsis> Architecture </span> <span class="md-nav__icon md-icon"></span> </a> </li> <li class="md-nav__item md-nav__item--pruned md-nav__item--nested"> <a href=../../mvp/mvp1/ class=md-nav__link> <span class=md-ellipsis> Project Management </span> <span class="md-nav__icon md-icon"></span> </a> </li> <li class="md-nav__item md-nav__item--pruned md-nav__item--nested"> <a href=../ class=md-nav__link> <span class=md-ellipsis> Progress 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class=md-nav__link> <span class=md-ellipsis> Sprint Overview </span> </a> </li> <li class=md-nav__item> <a href=#task-list class=md-nav__link> <span class=md-ellipsis> Task List </span> </a> <nav class=md-nav aria-label="Task List"> <ul class=md-nav__list> <li class=md-nav__item> <a href=#task-21-implement-live-llm-api-call-in-embeddingservice class=md-nav__link> <span class=md-ellipsis> Task 2.1: Implement Live LLM API Call in EmbeddingService </span> </a> </li> <li class=md-nav__item> <a href=#task-22-implement-real-vector-index-building-in-inmemorykg class=md-nav__link> <span class=md-ellipsis> Task 2.2: Implement Real Vector Index Building in InMemoryKG </span> </a> </li> <li class=md-nav__item> <a href=#task-23-implement-cosine-similarity-search-in-inmemorykg class=md-nav__link> <span class=md-ellipsis> Task 2.3: Implement Cosine Similarity Search in InMemoryKG </span> </a> </li> <li class=md-nav__item> <a href=#task-24-update-dependencies-run-all-checks class=md-nav__link> <span class=md-ellipsis> Task 2.4: Update Dependencies & Run All Checks </span> </a> </li> </ul> </nav> </li> <li class=md-nav__item> <a href=#end-of-sprint-2-review class=md-nav__link> <span class=md-ellipsis> End of Sprint 2 Review </span> </a> </li> <li class=md-nav__item> <a href=#implementation-guidance class=md-nav__link> <span class=md-ellipsis> Implementation Guidance </span> </a> <nav class=md-nav aria-label="Implementation Guidance"> <ul class=md-nav__list> <li class=md-nav__item> <a href=#task-21-implementation-details class=md-nav__link> <span class=md-ellipsis> Task 2.1 Implementation Details </span> </a> </li> <li class=md-nav__item> <a href=#task-22-implementation-details class=md-nav__link> <span class=md-ellipsis> Task 2.2 Implementation Details </span> </a> </li> <li class=md-nav__item> <a href=#task-23-implementation-details class=md-nav__link> <span class=md-ellipsis> Task 2.3 Implementation Details </span> </a> </li> </ul> </nav> </li> </ul> </nav> </div> </div> </div> <div class=md-content data-md-component=content> <article class="md-content__inner md-typeset"> <a href=https://github.com/BasalGanglia/kgraph-mcp-hackathon/edit/main/docs/progress/sprint2_plan.md title="Edit this page" class="md-content__button md-icon"> <svg xmlns=http://www.w3.org/2000/svg viewbox="0 0 24 24"><path d="M10 20H6V4h7v5h5v3.1l2-2V8l-6-6H6c-1.1 0-2 .9-2 2v16c0 1.1.9 2 2 2h4zm10.2-7c.1 0 .3.1.4.2l1.3 1.3c.2.2.2.6 0 .8l-1 1-2.1-2.1 1-1c.1-.1.2-.2.4-.2m0 3.9L14.1 23H12v-2.1l6.1-6.1z"/></svg> </a> <a href=https://github.com/BasalGanglia/kgraph-mcp-hackathon/raw/main/docs/progress/sprint2_plan.md title="View source of this page" class="md-content__button md-icon"> <svg xmlns=http://www.w3.org/2000/svg viewbox="0 0 24 24"><path d="M17 18c.56 0 1 .44 1 1s-.44 1-1 1-1-.44-1-1 .44-1 1-1m0-3c-2.73 0-5.06 1.66-6 4 .94 2.34 3.27 4 6 4s5.06-1.66 6-4c-.94-2.34-3.27-4-6-4m0 6.5a2.5 2.5 0 0 1-2.5-2.5 2.5 2.5 0 0 1 2.5-2.5 2.5 2.5 0 0 1 2.5 2.5 2.5 2.5 0 0 1-2.5 2.5M9.27 20H6V4h7v5h5v4.07c.7.08 1.36.25 2 .49V8l-6-6H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h4.5a8.2 8.2 0 0 1-1.23-2"/></svg> </a> <h1 id=sprint-2-mvp-1-real-embeddings-semantic-search-logic>Sprint 2 (MVP 1): Real Embeddings & Semantic Search Logic<a class=headerlink href=#sprint-2-mvp-1-real-embeddings-semantic-search-logic title="Permanent link">¶</a></h1> <h2 id=sprint-overview>Sprint Overview<a class=headerlink href=#sprint-overview title="Permanent link">¶</a></h2> <ul> <li><strong>Goal:</strong> Integrate actual LLM calls for generating embeddings, build the vector index within the <code>InMemoryKG</code>, and implement the core semantic search functionality. <em>Still no UI, focus on backend KG capabilities.</em></li> <li><strong>Duration:</strong> Estimated 3-5 hours (flexible within Hackathon Day 1, following Sprint 1).</li> <li><strong>Core Primitives Focused On:</strong> Tool (its description being embedded and searched).</li> <li><strong>Key Artifacts by End of Sprint:</strong></li> <li><code>kg_services/embedder.py</code>: <code>EmbeddingService.get_embedding</code> method now makes live API calls.</li> <li><code>kg_services/knowledge_graph.py</code>: <code>InMemoryKG.build_vector_index</code> now uses real embeddings, and <code>InMemoryKG.find_similar_tools</code> performs actual cosine similarity search.</li> <li>Updated unit tests, potentially including tests that mock the LLM API calls.</li> <li>Updated <code>requirements.txt</code> (if new LLM client libraries were added) and <code>requirements.lock</code>.</li> <li>All code linted, formatted, type-checked, and passing CI.</li> </ul> <h2 id=task-list>Task List<a class=headerlink href=#task-list title="Permanent link">¶</a></h2> <h3 id=task-21-implement-live-llm-api-call-in-embeddingservice>Task 2.1: Implement Live LLM API Call in <code>EmbeddingService</code><a class=headerlink href=#task-21-implement-live-llm-api-call-in-embeddingservice title="Permanent link">¶</a></h3> <ul> <li><strong>Status:</strong> Todo</li> <li><strong>Parent MVP:</strong> MVP 1</li> <li><strong>Parent Sprint (MVP 1):</strong> Sprint 2</li> <li><strong>Description:</strong> Modify <code>kg_services/embedder.py</code>'s <code>EmbeddingService.get_embedding</code> method to make actual API calls to your chosen LLM provider (OpenAI, Anthropic, or Azure OpenAI) to generate text embeddings.</li> <li>Ensure API keys are handled securely via environment variables (e.g., loaded using <code>python-dotenv</code> for local dev, and set as secrets in GitHub Actions/Hugging Face Spaces).</li> <li>Add necessary LLM client libraries (e.g., <code>openai</code>, <code>anthropic</code>) to <code>requirements.txt</code> if not already there.</li> <li><strong>Acceptance Criteria:</strong></li> <li><code>get_embedding</code> method successfully calls the chosen LLM API and returns a valid embedding vector (list of floats).</li> <li>Handles potential API errors gracefully (e.g., logs an error and returns <code>None</code> or raises a custom exception).</li> <li><code>requirements.txt</code> updated with LLM client library.</li> <li>Unit tests (with API mocking) pass.</li> <li><strong>TDD Approach:</strong> In <code>tests/kg_services/test_embedder.py</code>, refactor/add tests:</li> <li><code>test_get_embedding_live_success</code>: Mocks the LLM client's <code>create</code> (or equivalent) method to return a sample successful embedding response. Verifies the method processes this correctly.</li> <li><code>test_get_embedding_api_error</code>: Mocks the LLM client to raise an API error. Verifies <code>get_embedding</code> handles this gracefully.</li> </ul> <h3 id=task-22-implement-real-vector-index-building-in-inmemorykg>Task 2.2: Implement Real Vector Index Building in <code>InMemoryKG</code><a class=headerlink href=#task-22-implement-real-vector-index-building-in-inmemorykg title="Permanent link">¶</a></h3> <ul> <li><strong>Status:</strong> Todo</li> <li><strong>Parent MVP:</strong> MVP 1</li> <li><strong>Parent Sprint (MVP 1):</strong> Sprint 2</li> <li><strong>Description:</strong> Modify <code>kg_services/knowledge_graph.py</code>'s <code>InMemoryKG.build_vector_index</code> method.</li> <li>It should now iterate through the loaded <code>self.tools</code>.</li> <li>For each tool, construct a descriptive text string (e.g., from <code>name</code>, <code>description</code>, <code>tags</code>).</li> <li>Use the (now live) <code>EmbeddingService</code> instance to get a real embedding for this text.</li> <li>Store these real embeddings in <code>self.tool_embeddings</code> and corresponding <code>tool_id</code>s in <code>self.tool_ids_for_vectors</code>.</li> <li>Handle cases where <code>get_embedding</code> might return <code>None</code> (e.g., skip that tool or use a zero vector with a warning).</li> <li><strong>Acceptance Criteria:</strong></li> <li><code>build_vector_index</code> populates <code>self.tool_embeddings</code> with actual vectors from the LLM API.</li> <li>Correctly associates embeddings with <code>tool_id</code>s.</li> <li>Handles potential embedding failures for individual tools.</li> <li>Unit tests pass.</li> <li><strong>TDD Approach:</strong> In <code>tests/kg_services/test_knowledge_graph.py</code>:</li> <li><code>test_build_vector_index_with_real_embeddings</code>:<ul> <li>Needs a mock <code>EmbeddingService</code> that returns predictable (but distinct) vectors for different inputs.</li> <li>Load sample tools into <code>InMemoryKG</code>.</li> <li>Call <code>build_vector_index</code> with the mock embedder.</li> <li>Assert that <code>self.tool_embeddings</code> contains the expected number of vectors and that they match what the mock embedder would have returned.</li> <li>Assert <code>self.tool_ids_for_vectors</code> is populated correctly.</li> </ul> </li> <li><code>test_build_vector_index_handles_embedding_failure</code>:<ul> <li>Mock <code>EmbeddingService.get_embedding</code> to return <code>None</code> for one of the tools.</li> <li>Assert that the index is built for other tools and the failed one is handled (e.g., skipped or has a zero vector).</li> </ul> </li> </ul> <h3 id=task-23-implement-cosine-similarity-search-in-inmemorykg>Task 2.3: Implement Cosine Similarity Search in <code>InMemoryKG</code><a class=headerlink href=#task-23-implement-cosine-similarity-search-in-inmemorykg title="Permanent link">¶</a></h3> <ul> <li><strong>Status:</strong> Todo</li> <li><strong>Parent MVP:</strong> MVP 1</li> <li><strong>Parent Sprint (MVP 1):</strong> Sprint 2</li> <li><strong>Description:</strong> Modify <code>kg_services/knowledge_graph.py</code>'s <code>InMemoryKG.find_similar_tools</code> method.</li> <li>It should now use <code>numpy</code> to perform cosine similarity calculations between the input <code>query_embedding</code> and each of the real embeddings stored in <code>self.tool_embeddings</code>.</li> <li>Return the <code>tool_id</code>s of the <code>top_k</code> most similar tools.</li> <li>Ensure <code>numpy</code> is in <code>requirements.txt</code>.</li> <li><strong>Acceptance Criteria:</strong></li> <li><code>find_similar_tools</code> correctly calculates cosine similarities and returns the top_k tool IDs.</li> <li>Handles empty <code>self.tool_embeddings</code> case.</li> <li><code>numpy</code> is listed in <code>requirements.txt</code>.</li> <li>Unit tests pass.</li> <li><strong>TDD Approach:</strong> In <code>tests/kg_services/test_knowledge_graph.py</code>:</li> <li><code>test_cosine_similarity_calculation</code> (if <code>_cosine_similarity</code> is a helper, test it directly with known vectors).</li> <li><code>test_find_similar_tools_with_populated_index</code>:<ul> <li>Manually set <code>kg.tool_embeddings</code> and <code>kg.tool_ids_for_vectors</code> with a few known vectors and IDs.</li> <li>Provide a <code>query_embedding</code> that is known to be most similar to one of them.</li> <li>Call <code>find_similar_tools</code> and assert that the correct <code>tool_id</code>(s) are returned in the correct order.</li> </ul> </li> <li><code>test_find_similar_tools_empty_index</code>: Assert it returns an empty list.</li> <li><code>test_find_similar_tools_top_k_respected</code>: Test with different <code>top_k</code> values.</li> </ul> <h3 id=task-24-update-dependencies-run-all-checks>Task 2.4: Update Dependencies & Run All Checks<a class=headerlink href=#task-24-update-dependencies-run-all-checks title="Permanent link">¶</a></h3> <ul> <li><strong>Status:</strong> Todo</li> <li><strong>Parent MVP:</strong> MVP 1</li> <li><strong>Parent Sprint (MVP 1):</strong> Sprint 2</li> <li><strong>Description:</strong></li> <li>Ensure <code>requirements.txt</code> includes <code>openai</code> (or <code>anthropic</code>) and <code>numpy</code>.</li> <li>Ensure <code>requirements-dev.txt</code> includes <code>python-dotenv</code> and <code>unittest.mock</code> (mock is part of stdlib, but good to be aware if you were using an external mocking lib).</li> <li>Regenerate <code>requirements.lock</code>: <code>uv pip compile requirements.txt requirements-dev.txt --all-extras -o requirements.lock</code>.</li> <li>Run <code>just install</code> (or <code>uv pip sync requirements.lock</code>).</li> <li>Run <code>just lint</code>, <code>just format</code>, <code>just type-check</code>, <code>just test</code>.</li> <li>Commit all changes.</li> <li>Push to GitHub and verify CI pipeline passes. <em>Note: Live API calls in CI for tests are usually avoided. Ensure your tests for <code>EmbeddingService</code> use mocks. The <code>build_vector_index</code> tests should also use a mocked embedder.</em></li> <li><strong>Acceptance Criteria:</strong></li> <li><code>requirements.lock</code> is updated.</li> <li>All <code>just</code> checks pass locally.</li> <li>Code committed and pushed.</li> <li>GitHub Actions CI pipeline passes for the sprint's commits (leveraging mocks for API calls).</li> </ul> <h2 id=end-of-sprint-2-review>End of Sprint 2 Review<a class=headerlink href=#end-of-sprint-2-review title="Permanent link">¶</a></h2> <ul> <li><strong>What's Done:</strong></li> <li><code>EmbeddingService</code> can now generate real embeddings using an LLM API.</li> <li><code>InMemoryKG</code> can build a vector index using these real embeddings.</li> <li><code>InMemoryKG</code> can perform semantic search (cosine similarity) over the indexed tools.</li> <li>Unit tests cover the new functionalities, using mocks for external API calls.</li> <li>The backend logic for tool suggestion based on semantic similarity is complete.</li> <li><strong>What's Next (Sprint 3):</strong></li> <li>Implement the <code>SimplePlannerAgent</code> logic that ties together the <code>EmbeddingService</code> and <code>InMemoryKG</code> to process a user query and suggest tools.</li> <li><strong>Blockers/Issues:</strong></li> <li>API key setup and management (ensure it's smooth for local dev and CI doesn't expose keys).</li> <li>Potential rate limits or costs if generating many embeddings for testing (though for 3-5 tools, this should be minimal).</li> </ul> <h2 id=implementation-guidance>Implementation Guidance<a class=headerlink href=#implementation-guidance title="Permanent link">¶</a></h2> <h3 id=task-21-implementation-details>Task 2.1 Implementation Details<a class=headerlink href=#task-21-implementation-details title="Permanent link">¶</a></h3> <div class="language-python highlight"><pre><span></span><code><span id=__span-0-1><a id=__codelineno-0-1 name=__codelineno-0-1 href=#__codelineno-0-1></a><span class=c1># In kg_services/embedder.py</span> |
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</span><span id=__span-0-2><a id=__codelineno-0-2 name=__codelineno-0-2 href=#__codelineno-0-2></a><span class=c1># Refactor the EmbeddingService class:</span> |
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</span><span id=__span-0-3><a id=__codelineno-0-3 name=__codelineno-0-3 href=#__codelineno-0-3></a><span class=c1># - In __init__:</span> |
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</span><span id=__span-0-4><a id=__codelineno-0-4 name=__codelineno-0-4 href=#__codelineno-0-4></a><span class=c1># - Initialize the appropriate LLM client (OpenAI, AzureOpenAI, or Anthropic).</span> |
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</span><span id=__span-0-5><a id=__codelineno-0-5 name=__codelineno-0-5 href=#__codelineno-0-5></a><span class=c1># - Read API keys and any necessary endpoint/deployment information from environment variables.</span> |
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</span><span id=__span-0-6><a id=__codelineno-0-6 name=__codelineno-0-6 href=#__codelineno-0-6></a><span class=c1># - Add python-dotenv to requirements-dev.txt and load .env in __init__ if a .env file exists.</span> |
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</span><span id=__span-0-7><a id=__codelineno-0-7 name=__codelineno-0-7 href=#__codelineno-0-7></a><span class=c1># - In get_embedding(self, text: str) -> Optional[List[float]]:</span> |
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</span><span id=__span-0-8><a id=__codelineno-0-8 name=__codelineno-0-8 href=#__codelineno-0-8></a><span class=c1># - Replace the placeholder logic with an actual API call to the embedding endpoint.</span> |
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</span><span id=__span-0-9><a id=__codelineno-0-9 name=__codelineno-0-9 href=#__codelineno-0-9></a><span class=c1># - Include error handling (try-except block) for API calls.</span> |
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</span><span id=__span-0-10><a id=__codelineno-0-10 name=__codelineno-0-10 href=#__codelineno-0-10></a><span class=c1># - Ensure the text is preprocessed if necessary.</span> |
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</span></code></pre></div> <h3 id=task-22-implementation-details>Task 2.2 Implementation Details<a class=headerlink href=#task-22-implementation-details title="Permanent link">¶</a></h3> <div class="language-python highlight"><pre><span></span><code><span id=__span-1-1><a id=__codelineno-1-1 name=__codelineno-1-1 href=#__codelineno-1-1></a><span class=c1># In kg_services/knowledge_graph.py</span> |
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</span><span id=__span-1-2><a id=__codelineno-1-2 name=__codelineno-1-2 href=#__codelineno-1-2></a><span class=c1># Refactor InMemoryKG.build_vector_index(self, embedder: EmbeddingService):</span> |
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</span><span id=__span-1-3><a id=__codelineno-1-3 name=__codelineno-1-3 href=#__codelineno-1-3></a><span class=c1># - Clear self.tool_embeddings and self.tool_ids_for_vectors at the start.</span> |
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</span><span id=__span-1-4><a id=__codelineno-1-4 name=__codelineno-1-4 href=#__codelineno-1-4></a><span class=c1># - Iterate through self.tools.items().</span> |
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</span><span id=__span-1-5><a id=__codelineno-1-5 name=__codelineno-1-5 href=#__codelineno-1-5></a><span class=c1># - For each tool_id, tool:</span> |
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</span><span id=__span-1-6><a id=__codelineno-1-6 name=__codelineno-1-6 href=#__codelineno-1-6></a><span class=c1># - Construct a meaningful text: f"{tool.name} - {tool.description} Tags: {', '.join(tool.tags)}"</span> |
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</span><span id=__span-1-7><a id=__codelineno-1-7 name=__codelineno-1-7 href=#__codelineno-1-7></a><span class=c1># - Call embedding = embedder.get_embedding(text_to_embed)</span> |
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</span><span id=__span-1-8><a id=__codelineno-1-8 name=__codelineno-1-8 href=#__codelineno-1-8></a><span class=c1># - If embedding is not None and is valid:</span> |
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</span><span id=__span-1-9><a id=__codelineno-1-9 name=__codelineno-1-9 href=#__codelineno-1-9></a><span class=c1># - Append embedding to self.tool_embeddings</span> |
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</span><span id=__span-1-10><a id=__codelineno-1-10 name=__codelineno-1-10 href=#__codelineno-1-10></a><span class=c1># - Append tool_id to self.tool_ids_for_vectors</span> |
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</span><span id=__span-1-11><a id=__codelineno-1-11 name=__codelineno-1-11 href=#__codelineno-1-11></a><span class=c1># - Else (embedding failed):</span> |
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</span><span id=__span-1-12><a id=__codelineno-1-12 name=__codelineno-1-12 href=#__codelineno-1-12></a><span class=c1># - Log a warning</span> |
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</span><span id=__span-1-13><a id=__codelineno-1-13 name=__codelineno-1-13 href=#__codelineno-1-13></a><span class=c1># - Optionally, append a zero vector and the tool_id</span> |
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</span></code></pre></div> <h3 id=task-23-implementation-details>Task 2.3 Implementation Details<a class=headerlink href=#task-23-implementation-details title="Permanent link">¶</a></h3> <div class="language-python highlight"><pre><span></span><code><span id=__span-2-1><a id=__codelineno-2-1 name=__codelineno-2-1 href=#__codelineno-2-1></a><span class=c1># In kg_services/knowledge_graph.py</span> |
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</span><span id=__span-2-2><a id=__codelineno-2-2 name=__codelineno-2-2 href=#__codelineno-2-2></a><span class=c1># Refactor InMemoryKG._cosine_similarity(self, vec1: List[float], vec2: List[float]) -> float:</span> |
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</span><span id=__span-2-3><a id=__codelineno-2-3 name=__codelineno-2-3 href=#__codelineno-2-3></a><span class=c1># - Ensure inputs vec1 and vec2 are converted to np.array.</span> |
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</span><span id=__span-2-4><a id=__codelineno-2-4 name=__codelineno-2-4 href=#__codelineno-2-4></a><span class=c1># - Perform dot product.</span> |
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</span><span id=__span-2-5><a id=__codelineno-2-5 name=__codelineno-2-5 href=#__codelineno-2-5></a><span class=c1># - Calculate norms.</span> |
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</span><span id=__span-2-6><a id=__codelineno-2-6 name=__codelineno-2-6 href=#__codelineno-2-6></a><span class=c1># - Handle potential division by zero if a norm is zero (return 0.0 similarity).</span> |
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</span><span id=__span-2-7><a id=__codelineno-2-7 name=__codelineno-2-7 href=#__codelineno-2-7></a><span class=c1># - Return the cosine similarity.</span> |
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</span><span id=__span-2-8><a id=__codelineno-2-8 name=__codelineno-2-8 href=#__codelineno-2-8></a><span class=c1># Refactor InMemoryKG.find_similar_tools(self, query_embedding: List[float], top_k: int = 3) -> List[str]:</span> |
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</span><span id=__span-2-9><a id=__codelineno-2-9 name=__codelineno-2-9 href=#__codelineno-2-9></a><span class=c1># - If not self.tool_embeddings or not query_embedding, return [].</span> |
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</span><span id=__span-2-10><a id=__codelineno-2-10 name=__codelineno-2-10 href=#__codelineno-2-10></a><span class=c1># - Calculate similarities: Iterate through self.tool_embeddings, calling _cosine_similarity.</span> |
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</span><span id=__span-2-11><a id=__codelineno-2-11 name=__codelineno-2-11 href=#__codelineno-2-11></a><span class=c1># - Create pairs of (similarity_score, tool_id).</span> |
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</span><span id=__span-2-12><a id=__codelineno-2-12 name=__codelineno-2-12 href=#__codelineno-2-12></a><span class=c1># - Sort these pairs in descending order of similarity score.</span> |
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</span><span id=__span-2-13><a id=__codelineno-2-13 name=__codelineno-2-13 href=#__codelineno-2-13></a><span class=c1># - Return the tool_ids from the top top_k pairs.</span> |
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</span></code></pre></div> <aside class=md-source-file> <span class=md-source-file__fact> <span class=md-icon title="Last update"> <svg xmlns=http://www.w3.org/2000/svg viewbox="0 0 24 24"><path d="M21 13.1c-.1 0-.3.1-.4.2l-1 1 2.1 2.1 1-1c.2-.2.2-.6 0-.8l-1.3-1.3c-.1-.1-.2-.2-.4-.2m-1.9 1.8-6.1 6V23h2.1l6.1-6.1zM12.5 7v5.2l4 2.4-1 1L11 13V7zM11 21.9c-5.1-.5-9-4.8-9-9.9C2 6.5 6.5 2 12 2c5.3 0 9.6 4.1 10 9.3-.3-.1-.6-.2-1-.2s-.7.1-1 .2C19.6 7.2 16.2 4 12 4c-4.4 0-8 3.6-8 8 0 4.1 3.1 7.5 7.1 7.9l-.1.2z"/></svg> </span> <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date" title="June 7, 2025 10:50:33 UTC">June 7, 2025</span> </span> </aside> </article> </div> <script>var tabs=__md_get("__tabs");if(Array.isArray(tabs))e:for(var set of document.querySelectorAll(".tabbed-set")){var labels=set.querySelector(".tabbed-labels");for(var tab of tabs)for(var label of labels.getElementsByTagName("label"))if(label.innerText.trim()===tab){var input=document.getElementById(label.htmlFor);input.checked=!0;continue e}}</script> <script>var target=document.getElementById(location.hash.slice(1));target&&target.name&&(target.checked=target.name.startsWith("__tabbed_"))</script> </div> <button type=button class="md-top md-icon" data-md-component=top hidden> <svg xmlns=http://www.w3.org/2000/svg viewbox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8z"/></svg> Back to top </button> </main> <footer class=md-footer> <div class="md-footer-meta md-typeset"> <div class="md-footer-meta__inner md-grid"> <div class=md-copyright> <div class=md-copyright__highlight> Copyright © 2024 KGraph-MCP Development Team - 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