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[Oct Staging Branch] (#15460)
* Implement fix for thinking_blocks and converse API calls This fixes Claude's models via the Converse API, which should also fix Claude Code. * Add thinking literal * Fix mypy issues * Type fix for redacted thinking * Add voyage model integration in sagemaker * Add config file logic * Use already exiting voyage transformation * refactor code as per comments * fix merge error * refactor code as per comments * refactor code as per comments * UI new build * [Fix] router - regression when adding/removing models (#15451) * fix(router): update model_name_to_deployment_indices on deployment removal When a deployment is deleted, the model_name_to_deployment_indices map was not being updated, causing stale index references. This could lead to incorrect routing behavior when deployments with the same model_name were dynamically removed. Changes: - Update _update_deployment_indices_after_removal to maintain model_name_to_deployment_indices mapping - Remove deleted indices and decrement indices greater than removed index - Clean up empty entries when no deployments remain for a model name - Update test to verify proper index shifting and cleanup behavior * fix(router): remove redundant index building during initialization Remove duplicate index building operations that were causing unnecessary work during router initialization: 1. Removed redundant `_build_model_id_to_deployment_index_map` call in __init__ - `set_model_list` already builds all indices from scratch 2. Removed redundant `_build_model_name_index` call at end of `set_model_list` - the index is already built incrementally via `_create_deployment` -> `_add_model_to_list_and_index_map` Both indices (model_id_to_deployment_index_map and model_name_to_deployment_indices) are properly maintained as lookup indexes through existing helper methods. This change eliminates O(N) duplicate work during initialization without any behavioral changes. The indices continue to be correctly synchronized with model_list on all operations (add/remove/upsert). * fix(prometheus): Fix Prometheus metric collection in a multi-workers environment (#14929) Co-authored-by: sotazhang <sotazhang@tencent.com> * Add tiered pricing and cost calculation for xai * Use generic cost calculator * Resolve conflicts in generated HTML files * Remove penalty params as supported params for gemini preview model (#15503) * fix conversion of thinking block * add application level encryption in SQS (#15512) * docs: fix doc * docs(index.md): bump rc * [Fix] GEMINI - CLI - add google_routes to llm_api_routes (#15500) * fix: add google_routes to llm_api_routes * test: test_virtual_key_llm_api_routes_allows_google_routes * build: bump version * bump: version 1.78.0 → 1.78.1 * add application level encryption in SQS * add application level encryption in SQS --------- Co-authored-by: Krrish Dholakia <krrishdholakia@gmail.com> Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com> Co-authored-by: deepanshu <deepanshu.lulla@hq.bill.com> * [Feat] Bedrock Knowledgebase - return search_response when using /chat/completions API with LiteLLM (#15509) * docs: fix doc * docs(index.md): bump rc * [Fix] GEMINI - CLI - add google_routes to llm_api_routes (#15500) * fix: add google_routes to llm_api_routes * test: test_virtual_key_llm_api_routes_allows_google_routes * add AnthropicCitation * fix async_post_call_success_deployment_hook * fix add vector_store_custom_logger to global callbacks * test_e2e_bedrock_knowledgebase_retrieval_with_llm_api_call * async_post_call_success_deployment_hook * add async_post_call_streaming_deployment_hook * async def test_e2e_bedrock_knowledgebase_retrieval_with_llm_api_call_streaming(setup_vector_store_registry): * fix _call_post_streaming_deployment_hook * fix async_post_call_streaming_deployment_hook * test update * docs: Accessing Search Results * docs KB * fix chatUI * fix searchResults * fix onSearchResults * fix kb --------- Co-authored-by: Krrish Dholakia <krrishdholakia@gmail.com> * [Feat] Add dynamic rate limits on LiteLLM Gateway (#15518) * docs: fix doc * docs(index.md): bump rc * [Fix] GEMINI - CLI - add google_routes to llm_api_routes (#15500) * fix: add google_routes to llm_api_routes * test: test_virtual_key_llm_api_routes_allows_google_routes * build: bump version * bump: version 1.78.0 → 1.78.1 * fix: KeyRequestBase * fix rpm_limit_type * fix dynamic rate limits * fix use dynamic limits here * fix _should_enforce_rate_limit * fix _should_enforce_rate_limit * fix counter * test_dynamic_rate_limiting_v3 * use _create_rate_limit_descriptors --------- Co-authored-by: Krrish Dholakia <krrishdholakia@gmail.com> * Add google rerank endpoint * Add docs * fix mypy error * fix mypy and lint errors * Add haiku 4.5 integration * Add haiku 4.5 integration for other regions as well * Handle citation field correctly * Fix filtering headers for signature calcs * Add haiku 4.5 integration (#15650) --------- Co-authored-by: Leslie Cheng <leslie.cheng5@gmail.com> Co-authored-by: Sameer Kankute <sameer@berri.ai> Co-authored-by: Alexsander Hamir <alexsanderhamirgomesbaptista@gmail.com> Co-authored-by: Lucas <10226902+LoadingZhang@users.noreply.github.com> Co-authored-by: sotazhang <sotazhang@tencent.com> Co-authored-by: Deepanshu Lulla <deepanshu.lulla@gmail.com> Co-authored-by: Krrish Dholakia <krrishdholakia@gmail.com> Co-authored-by: deepanshu <deepanshu.lulla@hq.bill.com>
This commit is contained in:
Ishaan Jaff
GitHub
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commit
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143 changed files with 4011 additions and 302 deletions
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@ -412,6 +412,219 @@ This is sent to: `https://bedrock-agent-runtime.{aws_region}.amazonaws.com/knowl
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This process happens automatically whenever you include the `vector_store_ids` parameter in your request.
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## Accessing Search Results (Citations)
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When using vector stores, LiteLLM automatically returns search results in `provider_specific_fields`. This allows you to show users citations for the AI's response.
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### Key Concept
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Search results are always in: `response.choices[0].message.provider_specific_fields["search_results"]`
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For streaming: Results appear in the **final chunk** when `finish_reason == "stop"`
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### Non-Streaming Example
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**Non-Streaming Response with search results:**
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```json
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{
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"id": "chatcmpl-abc123",
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"choices": [{
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"index": 0,
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"message": {
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"role": "assistant",
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"content": "LiteLLM is a platform...",
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"provider_specific_fields": {
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"search_results": [{
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"search_query": "What is litellm?",
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"data": [{
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"score": 0.95,
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"content": [{"text": "...", "type": "text"}],
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"filename": "litellm-docs.md",
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"file_id": "doc-123"
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}]
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}]
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}
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},
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"finish_reason": "stop"
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}]
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}
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```
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<Tabs>
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<TabItem value="python-sdk" label="Python SDK">
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```python
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from openai import OpenAI
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client = OpenAI(
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base_url="http://localhost:4000",
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api_key="your-litellm-api-key"
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)
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response = client.chat.completions.create(
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model="claude-3-5-sonnet",
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messages=[{"role": "user", "content": "What is litellm?"}],
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tools=[{"type": "file_search", "vector_store_ids": ["T37J8R4WTM"]}]
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)
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# Get AI response
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print(response.choices[0].message.content)
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# Get search results (citations)
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search_results = response.choices[0].message.provider_specific_fields.get("search_results", [])
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for result_page in search_results:
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for idx, item in enumerate(result_page['data'], 1):
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print(f"[{idx}] {item.get('filename', 'Unknown')} (score: {item['score']:.2f})")
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```
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</TabItem>
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<TabItem value="typescript" label="TypeScript SDK">
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```typescript
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import OpenAI from 'openai';
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const client = new OpenAI({
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baseURL: 'http://localhost:4000',
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apiKey: process.env.LITELLM_API_KEY
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});
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const response = await client.chat.completions.create({
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model: 'claude-3-5-sonnet',
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messages: [{ role: 'user', content: 'What is litellm?' }],
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tools: [{ type: 'file_search', vector_store_ids: ['T37J8R4WTM'] }]
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});
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// Get AI response
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console.log(response.choices[0].message.content);
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// Get search results (citations)
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const message = response.choices[0].message as any;
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const searchResults = message.provider_specific_fields?.search_results || [];
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searchResults.forEach((page: any) => {
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page.data.forEach((item: any, idx: number) => {
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console.log(`[${idx + 1}] ${item.filename || 'Unknown'} (${item.score.toFixed(2)})`);
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});
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});
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```
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</TabItem>
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</Tabs>
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### Streaming Example
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**Streaming Response with search results (final chunk):**
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```json
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{
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"id": "chatcmpl-abc123",
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"choices": [{
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"index": 0,
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"delta": {
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"provider_specific_fields": {
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"search_results": [{
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"search_query": "What is litellm?",
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"data": [{
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"score": 0.95,
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"content": [{"text": "...", "type": "text"}],
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"filename": "litellm-docs.md",
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"file_id": "doc-123"
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}]
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}]
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}
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},
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"finish_reason": "stop"
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}]
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}
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```
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<Tabs>
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<TabItem value="python-sdk" label="Python SDK">
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```python
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from openai import OpenAI
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client = OpenAI(
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base_url="http://localhost:4000",
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api_key="your-litellm-api-key"
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)
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stream = client.chat.completions.create(
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model="claude-3-5-sonnet",
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messages=[{"role": "user", "content": "What is litellm?"}],
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tools=[{"type": "file_search", "vector_store_ids": ["T37J8R4WTM"]}],
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stream=True
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)
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for chunk in stream:
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# Stream content
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if chunk.choices[0].delta.content:
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print(chunk.choices[0].delta.content, end="", flush=True)
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# Get citations in final chunk
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if chunk.choices[0].finish_reason == "stop":
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search_results = getattr(chunk.choices[0].delta, 'provider_specific_fields', {}).get('search_results', [])
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if search_results:
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print("\n\nSources:")
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for page in search_results:
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for idx, item in enumerate(page['data'], 1):
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print(f" [{idx}] {item.get('filename', 'Unknown')} ({item['score']:.2f})")
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```
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</TabItem>
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<TabItem value="typescript" label="TypeScript SDK">
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```typescript
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import OpenAI from 'openai';
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const stream = await client.chat.completions.create({
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model: 'claude-3-5-sonnet',
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messages: [{ role: 'user', content: 'What is litellm?' }],
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tools: [{ type: 'file_search', vector_store_ids: ['T37J8R4WTM'] }],
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stream: true
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});
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for await (const chunk of stream) {
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// Stream content
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if (chunk.choices[0]?.delta?.content) {
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process.stdout.write(chunk.choices[0].delta.content);
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}
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// Get citations in final chunk
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if (chunk.choices[0]?.finish_reason === 'stop') {
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const searchResults = (chunk.choices[0].delta as any).provider_specific_fields?.search_results || [];
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if (searchResults.length > 0) {
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console.log('\n\nSources:');
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searchResults.forEach((page: any) => {
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page.data.forEach((item: any, idx: number) => {
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console.log(` [${idx + 1}] ${item.filename || 'Unknown'} (${item.score.toFixed(2)})`);
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});
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});
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}
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}
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}
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```
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</TabItem>
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</Tabs>
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### Search Result Fields
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| Field | Type | Description |
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|-------|------|-------------|
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| `search_query` | string | The query used to search the vector store |
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| `data` | array | Array of search results |
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| `data[].score` | float | Relevance score (0-1, higher is more relevant) |
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| `data[].content` | array | Content chunks with `text` and `type` |
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| `data[].filename` | string | Name of the source file (optional) |
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| `data[].file_id` | string | Identifier for the source file (optional) |
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| `data[].attributes` | object | Provider-specific metadata (optional) |
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## API Reference
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### LiteLLM Completion Knowledge Base Parameters
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@ -138,7 +138,14 @@ print(response.choices[0].message.content)
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</Tabs>
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Futher Reading Vector Stores:
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## Accessing Search Results
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See how to access vector store search results in your response:
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- [Accessing Search Results (Non-Streaming & Streaming)](../completion/knowledgebase#accessing-search-results-citations)
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## Further Reading
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Vector Stores:
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- [Always on Vector Stores](https://docs.litellm.ai/docs/completion/knowledgebase#always-on-for-a-model)
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- [Listing available vector stores on litellm proxy](https://docs.litellm.ai/docs/completion/knowledgebase#listing-available-vector-stores)
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- [How LiteLLM Vector Stores Work](https://docs.litellm.ai/docs/completion/knowledgebase#how-it-works)
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@ -12,7 +12,7 @@ import TabItem from '@theme/TabItem';
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| Provider Route on LiteLLM | `vertex_ai/` |
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| Link to Provider Doc | [Vertex AI ↗](https://cloud.google.com/vertex-ai) |
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| Base URL | 1. Regional endpoints<br/>`https://{vertex_location}-aiplatform.googleapis.com/`<br/>2. Global endpoints (limited availability)<br/>`https://aiplatform.googleapis.com/`|
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| Supported Operations | [`/chat/completions`](#sample-usage), `/completions`, [`/embeddings`](#embedding-models), [`/audio/speech`](#text-to-speech-apis), [`/fine_tuning`](#fine-tuning-apis), [`/batches`](#batch-apis), [`/files`](#batch-apis), [`/images`](#image-generation-models) |
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| Supported Operations | [`/chat/completions`](#sample-usage), `/completions`, [`/embeddings`](#embedding-models), [`/audio/speech`](#text-to-speech-apis), [`/fine_tuning`](#fine-tuning-apis), [`/batches`](#batch-apis), [`/files`](#batch-apis), [`/images`](#image-generation-models), [`/rerank`](#rerank-api) |
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<br />
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@ -3114,3 +3114,101 @@ Once that's done, when you deploy the new container in the Google Cloud Run serv
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s/o @[Darien Kindlund](https://www.linkedin.com/in/kindlund/) for this tutorial
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## **Rerank API**
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Vertex AI supports reranking through the Discovery Engine API, providing semantic ranking capabilities for document retrieval.
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### Setup
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Set your Google Cloud project ID:
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```bash
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export VERTEXAI_PROJECT="your-project-id"
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```
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### Usage
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```python
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from litellm import rerank
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# Using the latest model (recommended)
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response = rerank(
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model="vertex_ai/semantic-ranker-default@latest",
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query="What is Google Gemini?",
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documents=[
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"Gemini is a cutting edge large language model created by Google.",
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"The Gemini zodiac symbol often depicts two figures standing side-by-side.",
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"Gemini is a constellation that can be seen in the night sky."
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],
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top_n=2,
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return_documents=True # Set to False for ID-only responses
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)
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# Using specific model versions
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response_v003 = rerank(
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model="vertex_ai/semantic-ranker-default-003",
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query="What is Google Gemini?",
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documents=documents,
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top_n=2
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)
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print(response.results)
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```
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### Parameters
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| Parameter | Type | Description |
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|-----------|------|-------------|
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| `model` | string | Model name (e.g., `vertex_ai/semantic-ranker-default@latest`) |
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| `query` | string | Search query |
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| `documents` | list | Documents to rank |
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| `top_n` | int | Number of top results to return |
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| `return_documents` | bool | Return full content (True) or IDs only (False) |
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### Supported Models
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- `semantic-ranker-default@latest`
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- `semantic-ranker-fast@latest`
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- `semantic-ranker-default-003`
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- `semantic-ranker-default-002`
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For detailed model specifications, see the [Google Cloud ranking API documentation](https://cloud.google.com/generative-ai-app-builder/docs/ranking#rank_or_rerank_a_set_of_records_according_to_a_query).
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### Proxy Usage
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Add to your `config.yaml`:
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```yaml
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model_list:
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- model_name: semantic-ranker-default@latest
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litellm_params:
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model: vertex_ai/semantic-ranker-default@latest
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vertex_ai_project: "your-project-id"
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vertex_ai_location: "us-central1"
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vertex_ai_credentials: "path/to/service-account.json"
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```
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Start the proxy:
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```bash
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litellm --config /path/to/config.yaml
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```
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Test with curl:
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```bash
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curl http://0.0.0.0:4000/rerank \
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-H "Authorization: Bearer sk-1234" \
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-H "Content-Type: application/json" \
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-d '{
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"model": "semantic-ranker-default@latest",
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"query": "What is Google Gemini?",
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"documents": [
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"Gemini is a cutting edge large language model created by Google.",
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"The Gemini zodiac symbol often depicts two figures standing side-by-side.",
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"Gemini is a constellation that can be seen in the night sky."
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],
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"top_n": 2
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}'
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```
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@ -121,4 +121,5 @@ curl http://0.0.0.0:4000/rerank \
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| HuggingFace| [Usage](../docs/providers/huggingface_rerank) |
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| Infinity| [Usage](../docs/providers/infinity) |
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| vLLM| [Usage](../docs/providers/vllm#rerank-endpoint) |
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| DeepInfra| [Usage](../docs/providers/deepinfra#rerank-endpoint) |
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| DeepInfra| [Usage](../docs/providers/deepinfra#rerank-endpoint) |
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| Vertex AI| [Usage](../docs/providers/vertex#rerank-api) |
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@ -1,6 +1,7 @@
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# used for /metrics endpoint on LiteLLM Proxy
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#### What this does ####
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# On success, log events to Prometheus
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import os
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import sys
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from datetime import datetime, timedelta
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from typing import (
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@ -2211,7 +2212,13 @@ class PrometheusLogger(CustomLogger):
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)
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# Create metrics ASGI app
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metrics_app = make_asgi_app()
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if 'PROMETHEUS_MULTIPROC_DIR' in os.environ:
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from prometheus_client import CollectorRegistry, multiprocess
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registry = CollectorRegistry()
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multiprocess.MultiProcessCollector(registry)
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metrics_app = make_asgi_app(registry)
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else:
|
||||
metrics_app = make_asgi_app()
|
||||
|
||||
# Mount the metrics app to the app
|
||||
app.mount("/metrics", metrics_app)
|
||||
|
|
@ -2354,7 +2361,6 @@ def get_custom_labels_from_tags(tags: List[str]) -> Dict[str, str]:
|
|||
}
|
||||
"""
|
||||
|
||||
from litellm.router_utils.pattern_match_deployments import PatternMatchRouter
|
||||
from litellm.types.integrations.prometheus import _sanitize_prometheus_label_name
|
||||
|
||||
configured_tags = litellm.custom_prometheus_tags
|
||||
|
|
@ -2362,7 +2368,6 @@ def get_custom_labels_from_tags(tags: List[str]) -> Dict[str, str]:
|
|||
return {}
|
||||
|
||||
result: Dict[str, str] = {}
|
||||
pattern_router = PatternMatchRouter()
|
||||
|
||||
for configured_tag in configured_tags:
|
||||
label_name = _sanitize_prometheus_label_name(f"tag_{configured_tag}")
|
||||
|
|
|
|||
|
|
@ -1071,6 +1071,7 @@ from .llms.infinity.rerank.transformation import InfinityRerankConfig
|
|||
from .llms.jina_ai.rerank.transformation import JinaAIRerankConfig
|
||||
from .llms.deepinfra.rerank.transformation import DeepinfraRerankConfig
|
||||
from .llms.nvidia_nim.rerank.transformation import NvidiaNimRerankConfig
|
||||
from .llms.vertex_ai.rerank.transformation import VertexAIRerankConfig
|
||||
from .llms.clarifai.chat.transformation import ClarifaiConfig
|
||||
from .llms.ai21.chat.transformation import AI21ChatConfig, AI21ChatConfig as AI21Config
|
||||
from .llms.meta_llama.chat.transformation import LlamaAPIConfig
|
||||
|
|
|
|||
|
|
@ -17,6 +17,7 @@ DEFAULT_SQS_FLUSH_INTERVAL_SECONDS = int(
|
|||
DEFAULT_NUM_WORKERS_LITELLM_PROXY = int(
|
||||
os.getenv("DEFAULT_NUM_WORKERS_LITELLM_PROXY", 1)
|
||||
)
|
||||
DYNAMIC_RATE_LIMIT_ERROR_THRESHOLD_PER_MINUTE = int(os.getenv("DYNAMIC_RATE_LIMIT_ERROR_THRESHOLD_PER_MINUTE", 1))
|
||||
DEFAULT_SQS_BATCH_SIZE = int(os.getenv("DEFAULT_SQS_BATCH_SIZE", 512))
|
||||
SQS_SEND_MESSAGE_ACTION = "SendMessage"
|
||||
SQS_API_VERSION = "2012-11-05"
|
||||
|
|
|
|||
|
|
@ -204,6 +204,19 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac
|
|||
"""
|
||||
pass
|
||||
|
||||
async def async_post_call_streaming_deployment_hook(
|
||||
self,
|
||||
request_data: dict,
|
||||
response_chunk: Any,
|
||||
call_type: Optional[CallTypes],
|
||||
) -> Optional[Any]:
|
||||
"""
|
||||
Allow modifying streaming chunks just before they're returned to the user.
|
||||
|
||||
This is called for each streaming chunk in the response.
|
||||
"""
|
||||
pass
|
||||
|
||||
#### Fallback Events - router/proxy only ####
|
||||
async def log_model_group_rate_limit_error(
|
||||
self, exception: Exception, original_model_group: Optional[str], kwargs: dict
|
||||
|
|
|
|||
|
|
@ -7,6 +7,8 @@ This logger sends ``StandardLoggingPayload`` entries to an AWS SQS queue.
|
|||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import base64
|
||||
import json
|
||||
import traceback
|
||||
from typing import List, Optional
|
||||
|
||||
|
|
@ -27,31 +29,37 @@ from litellm.llms.custom_httpx.http_handler import (
|
|||
from litellm.types.utils import StandardLoggingPayload
|
||||
|
||||
from .custom_batch_logger import CustomBatchLogger
|
||||
from litellm.litellm_core_utils.app_crypto import AppCrypto
|
||||
|
||||
|
||||
class SQSLogger(CustomBatchLogger, BaseAWSLLM):
|
||||
"""Batching logger that writes logs to an AWS SQS queue."""
|
||||
"""Batching logger that writes logs to an AWS SQS queue, optionally encrypting the payload."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
sqs_queue_url: Optional[str] = None,
|
||||
sqs_region_name: Optional[str] = None,
|
||||
sqs_api_version: Optional[str] = None,
|
||||
sqs_use_ssl: bool = True,
|
||||
sqs_verify: Optional[bool] = None,
|
||||
sqs_endpoint_url: Optional[str] = None,
|
||||
sqs_aws_access_key_id: Optional[str] = None,
|
||||
sqs_aws_secret_access_key: Optional[str] = None,
|
||||
sqs_aws_session_token: Optional[str] = None,
|
||||
sqs_aws_session_name: Optional[str] = None,
|
||||
sqs_aws_profile_name: Optional[str] = None,
|
||||
sqs_aws_role_name: Optional[str] = None,
|
||||
sqs_aws_web_identity_token: Optional[str] = None,
|
||||
sqs_aws_sts_endpoint: Optional[str] = None,
|
||||
sqs_flush_interval: Optional[int] = DEFAULT_SQS_FLUSH_INTERVAL_SECONDS,
|
||||
sqs_batch_size: Optional[int] = DEFAULT_SQS_BATCH_SIZE,
|
||||
sqs_config=None,
|
||||
**kwargs,
|
||||
self,
|
||||
# --- Standard SQS params ---
|
||||
sqs_queue_url: Optional[str] = None,
|
||||
sqs_region_name: Optional[str] = None,
|
||||
sqs_api_version: Optional[str] = None,
|
||||
sqs_use_ssl: bool = True,
|
||||
sqs_verify: Optional[bool] = None,
|
||||
sqs_endpoint_url: Optional[str] = None,
|
||||
sqs_aws_access_key_id: Optional[str] = None,
|
||||
sqs_aws_secret_access_key: Optional[str] = None,
|
||||
sqs_aws_session_token: Optional[str] = None,
|
||||
sqs_aws_session_name: Optional[str] = None,
|
||||
sqs_aws_profile_name: Optional[str] = None,
|
||||
sqs_aws_role_name: Optional[str] = None,
|
||||
sqs_aws_web_identity_token: Optional[str] = None,
|
||||
sqs_aws_sts_endpoint: Optional[str] = None,
|
||||
sqs_flush_interval: Optional[int] = DEFAULT_SQS_FLUSH_INTERVAL_SECONDS,
|
||||
sqs_batch_size: Optional[int] = DEFAULT_SQS_BATCH_SIZE,
|
||||
sqs_config=None,
|
||||
# --- 🔐 Application-level encryption params ---
|
||||
sqs_aws_use_application_level_encryption: bool = False,
|
||||
sqs_app_encryption_key_b64: Optional[str] = None,
|
||||
sqs_app_encryption_aad: Optional[str] = None,
|
||||
**kwargs,
|
||||
) -> None:
|
||||
try:
|
||||
verbose_logger.debug(
|
||||
|
|
@ -77,7 +85,11 @@ class SQSLogger(CustomBatchLogger, BaseAWSLLM):
|
|||
sqs_aws_role_name=sqs_aws_role_name,
|
||||
sqs_aws_web_identity_token=sqs_aws_web_identity_token,
|
||||
sqs_aws_sts_endpoint=sqs_aws_sts_endpoint,
|
||||
sqs_aws_use_application_level_encryption=sqs_aws_use_application_level_encryption,
|
||||
sqs_app_encryption_key_b64=sqs_app_encryption_key_b64,
|
||||
sqs_app_encryption_aad=sqs_app_encryption_aad,
|
||||
sqs_config=sqs_config,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
asyncio.create_task(self.periodic_flush())
|
||||
|
|
@ -95,7 +107,6 @@ class SQSLogger(CustomBatchLogger, BaseAWSLLM):
|
|||
)
|
||||
|
||||
self.log_queue: List[StandardLoggingPayload] = []
|
||||
|
||||
BaseAWSLLM.__init__(self)
|
||||
|
||||
except Exception as e:
|
||||
|
|
@ -118,6 +129,9 @@ class SQSLogger(CustomBatchLogger, BaseAWSLLM):
|
|||
sqs_aws_role_name: Optional[str] = None,
|
||||
sqs_aws_web_identity_token: Optional[str] = None,
|
||||
sqs_aws_sts_endpoint: Optional[str] = None,
|
||||
sqs_aws_use_application_level_encryption: bool = False,
|
||||
sqs_app_encryption_key_b64: Optional[str] = None,
|
||||
sqs_app_encryption_aad: Optional[str] = None,
|
||||
sqs_config=None,
|
||||
) -> None:
|
||||
litellm.aws_sqs_callback_params = litellm.aws_sqs_callback_params or {}
|
||||
|
|
@ -179,6 +193,27 @@ class SQSLogger(CustomBatchLogger, BaseAWSLLM):
|
|||
litellm.aws_sqs_callback_params.get("sqs_aws_sts_endpoint") or sqs_aws_sts_endpoint
|
||||
)
|
||||
|
||||
self.sqs_aws_use_application_level_encryption = (
|
||||
litellm.aws_sqs_callback_params.get("sqs_aws_use_application_level_encryption", False)
|
||||
or sqs_aws_use_application_level_encryption
|
||||
)
|
||||
self.sqs_app_encryption_key_b64 = (
|
||||
litellm.aws_sqs_callback_params.get("sqs_app_encryption_key_b64")
|
||||
or sqs_app_encryption_key_b64
|
||||
)
|
||||
self.sqs_app_encryption_aad = (
|
||||
litellm.aws_sqs_callback_params.get("sqs_app_encryption_aad")
|
||||
or sqs_app_encryption_aad
|
||||
)
|
||||
self.app_crypto: Optional[AppCrypto] = None
|
||||
if self.sqs_aws_use_application_level_encryption:
|
||||
if not self.sqs_app_encryption_key_b64:
|
||||
raise ValueError("sqs_app_encryption_key_b64 is required when encryption is enabled.")
|
||||
key = base64.b64decode(self.sqs_app_encryption_key_b64)
|
||||
self.app_crypto = AppCrypto(key)
|
||||
verbose_logger.debug(
|
||||
"SQSLogger: Application-level encryption enabled."
|
||||
)
|
||||
self.sqs_config = litellm.aws_sqs_callback_params.get("sqs_config") or sqs_config
|
||||
|
||||
async def async_log_success_event(
|
||||
|
|
@ -256,11 +291,21 @@ class SQSLogger(CustomBatchLogger, BaseAWSLLM):
|
|||
if self.sqs_queue_url is None:
|
||||
raise ValueError("sqs_queue_url not set")
|
||||
|
||||
json_string = safe_dumps(payload)
|
||||
json_data = json.loads(safe_dumps(payload))
|
||||
if self.app_crypto:
|
||||
aad_bytes = (
|
||||
self.sqs_app_encryption_aad.encode("utf-8")
|
||||
if self.sqs_app_encryption_aad
|
||||
else None
|
||||
)
|
||||
encrypted = self.app_crypto.encrypt_json(json_data, aad=aad_bytes)
|
||||
json_string = json.dumps({"__encrypted__": True, "payload": encrypted})
|
||||
else:
|
||||
json_string = safe_dumps(payload)
|
||||
|
||||
body = (
|
||||
f"Action={SQS_SEND_MESSAGE_ACTION}&Version={SQS_API_VERSION}&MessageBody="
|
||||
+ quote(json_string, safe="")
|
||||
f"Action={SQS_SEND_MESSAGE_ACTION}&Version={SQS_API_VERSION}&MessageBody="
|
||||
+ quote(json_string, safe="")
|
||||
)
|
||||
|
||||
headers = {
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ This hook is called before making an LLM request when a vector store is configur
|
|||
It searches the vector store for relevant context and appends it to the messages.
|
||||
"""
|
||||
|
||||
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, cast
|
||||
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, cast
|
||||
|
||||
import litellm
|
||||
import litellm.vector_stores
|
||||
|
|
@ -88,6 +88,8 @@ class VectorStorePreCallHook(CustomLogger):
|
|||
return model, messages, non_default_params
|
||||
|
||||
modified_messages: List[AllMessageValues] = messages.copy()
|
||||
all_search_results: List[VectorStoreSearchResponse] = []
|
||||
|
||||
for vector_store_to_run in vector_stores_to_run:
|
||||
|
||||
# Get vector store id from the vector store config
|
||||
|
|
@ -104,6 +106,8 @@ class VectorStorePreCallHook(CustomLogger):
|
|||
|
||||
verbose_logger.debug(f"search_response: {search_response}")
|
||||
|
||||
# Store search results for later use in citations
|
||||
all_search_results.append(search_response)
|
||||
|
||||
# Process search results and append as context
|
||||
modified_messages = self._append_search_results_to_messages(
|
||||
|
|
@ -115,6 +119,10 @@ class VectorStorePreCallHook(CustomLogger):
|
|||
num_results = 0
|
||||
num_results = len(search_response.get("data", []) or [])
|
||||
verbose_logger.debug(f"Vector store search completed. Added context from {num_results} results")
|
||||
|
||||
# Store search results as-is (already in OpenAI-compatible format)
|
||||
if litellm_logging_obj and all_search_results:
|
||||
litellm_logging_obj.model_call_details["search_results"] = all_search_results
|
||||
|
||||
return model, modified_messages, non_default_params
|
||||
|
||||
|
|
@ -194,3 +202,109 @@ class VectorStorePreCallHook(CustomLogger):
|
|||
return modified_messages
|
||||
|
||||
return messages
|
||||
|
||||
async def async_post_call_success_deployment_hook(
|
||||
self,
|
||||
request_data: dict,
|
||||
response: Any,
|
||||
call_type: Optional[Any],
|
||||
) -> Optional[Any]:
|
||||
"""
|
||||
Add search results to the response after successful LLM call.
|
||||
|
||||
This hook adds the vector store search results (already in OpenAI-compatible format)
|
||||
to the response's provider_specific_fields.
|
||||
"""
|
||||
try:
|
||||
verbose_logger.debug("VectorStorePreCallHook.async_post_call_success_deployment_hook called")
|
||||
|
||||
# Get logging object from request_data
|
||||
litellm_logging_obj = request_data.get("litellm_logging_obj")
|
||||
if not litellm_logging_obj:
|
||||
verbose_logger.debug("No litellm_logging_obj in request_data")
|
||||
return None
|
||||
|
||||
verbose_logger.debug(f"model_call_details keys: {list(litellm_logging_obj.model_call_details.keys())}")
|
||||
|
||||
# Get search results from model_call_details (already in OpenAI format)
|
||||
search_results: Optional[List[VectorStoreSearchResponse]] = (
|
||||
litellm_logging_obj.model_call_details.get("search_results")
|
||||
)
|
||||
|
||||
verbose_logger.debug(f"Search results found: {search_results is not None}")
|
||||
|
||||
if not search_results:
|
||||
verbose_logger.debug("No search results found")
|
||||
return None
|
||||
|
||||
# Add search results to response object
|
||||
if hasattr(response, "choices") and response.choices:
|
||||
for choice in response.choices:
|
||||
if hasattr(choice, "message") and choice.message:
|
||||
# Get existing provider_specific_fields or create new dict
|
||||
provider_fields = getattr(choice.message, "provider_specific_fields", None) or {}
|
||||
|
||||
# Add search results (already in OpenAI-compatible format)
|
||||
provider_fields["search_results"] = search_results
|
||||
|
||||
# Set the provider_specific_fields
|
||||
setattr(choice.message, "provider_specific_fields", provider_fields)
|
||||
|
||||
verbose_logger.debug(f"Added {len(search_results)} search results to response")
|
||||
|
||||
# Return modified response
|
||||
return response
|
||||
|
||||
except Exception as e:
|
||||
verbose_logger.exception(f"Error adding search results to response: {str(e)}")
|
||||
# Don't fail the request if search results fail to be added
|
||||
return None
|
||||
|
||||
async def async_post_call_streaming_deployment_hook(
|
||||
self,
|
||||
request_data: dict,
|
||||
response_chunk: Any,
|
||||
call_type: Optional[Any],
|
||||
) -> Optional[Any]:
|
||||
"""
|
||||
Add search results to the final streaming chunk.
|
||||
|
||||
This hook is called for the final streaming chunk, allowing us to add
|
||||
search results to the stream before it's returned to the user.
|
||||
"""
|
||||
try:
|
||||
verbose_logger.debug("VectorStorePreCallHook.async_post_call_streaming_deployment_hook called")
|
||||
|
||||
# Get search results from model_call_details (already in OpenAI format)
|
||||
search_results: Optional[List[VectorStoreSearchResponse]] = (
|
||||
request_data.get("search_results")
|
||||
)
|
||||
|
||||
verbose_logger.debug(f"Search results found for streaming chunk: {search_results is not None}")
|
||||
|
||||
if not search_results:
|
||||
verbose_logger.debug("No search results found for streaming chunk")
|
||||
return response_chunk
|
||||
|
||||
# Add search results to streaming chunk
|
||||
if hasattr(response_chunk, "choices") and response_chunk.choices:
|
||||
for choice in response_chunk.choices:
|
||||
if hasattr(choice, "delta") and choice.delta:
|
||||
# Get existing provider_specific_fields or create new dict
|
||||
provider_fields = getattr(choice.delta, "provider_specific_fields", None) or {}
|
||||
|
||||
# Add search results (already in OpenAI-compatible format)
|
||||
provider_fields["search_results"] = search_results
|
||||
|
||||
# Set the provider_specific_fields
|
||||
choice.delta.provider_specific_fields = provider_fields
|
||||
|
||||
verbose_logger.debug(f"Added {len(search_results)} search results to streaming chunk")
|
||||
|
||||
# Return modified chunk
|
||||
return response_chunk
|
||||
|
||||
except Exception as e:
|
||||
verbose_logger.exception(f"Error adding search results to streaming chunk: {str(e)}")
|
||||
# Don't fail the request if search results fail to be added
|
||||
return response_chunk
|
||||
|
|
|
|||
31
litellm/litellm_core_utils/app_crypto.py
Normal file
31
litellm/litellm_core_utils/app_crypto.py
Normal file
|
|
@ -0,0 +1,31 @@
|
|||
import base64
|
||||
import json
|
||||
import os
|
||||
|
||||
from cryptography.hazmat.primitives.ciphers.aead import AESGCM
|
||||
|
||||
class AppCrypto:
|
||||
def __init__(self, master_key: bytes):
|
||||
if len(master_key) != 32:
|
||||
raise ValueError("Master key must be 32 bytes for AES-256-GCM")
|
||||
self.key = master_key
|
||||
|
||||
def encrypt_json(self, data: dict, aad: bytes | None = None) -> dict:
|
||||
aes = AESGCM(self.key)
|
||||
nonce = os.urandom(12)
|
||||
plaintext = json.dumps(data).encode("utf-8")
|
||||
ct = aes.encrypt(nonce, plaintext, aad)
|
||||
ciphertext, tag = ct[:-16], ct[-16:]
|
||||
return {
|
||||
"nonce": base64.b64encode(nonce).decode(),
|
||||
"ciphertext": base64.b64encode(ciphertext).decode(),
|
||||
"tag": base64.b64encode(tag).decode(),
|
||||
}
|
||||
|
||||
def decrypt_json(self, enc: dict, aad: bytes | None = None) -> dict:
|
||||
aes = AESGCM(self.key)
|
||||
nonce = base64.b64decode(enc["nonce"])
|
||||
ct = base64.b64decode(enc["ciphertext"])
|
||||
tag = base64.b64decode(enc["tag"])
|
||||
data = aes.decrypt(nonce, ct + tag, aad)
|
||||
return json.loads(data.decode())
|
||||
|
|
@ -699,6 +699,9 @@ class Logging(LiteLLMLoggingBaseClass):
|
|||
self.model_call_details["prompt_integration"] = (
|
||||
vector_store_custom_logger.__class__.__name__
|
||||
)
|
||||
# Add to global callbacks so post-call hooks are invoked
|
||||
if vector_store_custom_logger and vector_store_custom_logger not in litellm.callbacks:
|
||||
litellm.logging_callback_manager.add_litellm_callback(vector_store_custom_logger)
|
||||
return vector_store_custom_logger
|
||||
|
||||
return None
|
||||
|
|
|
|||
|
|
@ -20,7 +20,9 @@ from litellm.litellm_core_utils.redact_messages import LiteLLMLoggingObject
|
|||
from litellm.litellm_core_utils.thread_pool_executor import executor
|
||||
from litellm.types.llms.openai import ChatCompletionChunk
|
||||
from litellm.types.router import GenericLiteLLMParams
|
||||
from litellm.types.utils import Delta
|
||||
from litellm.types.utils import (
|
||||
Delta,
|
||||
)
|
||||
from litellm.types.utils import GenericStreamingChunk as GChunk
|
||||
from litellm.types.utils import (
|
||||
ModelResponse,
|
||||
|
|
@ -1520,6 +1522,43 @@ class CustomStreamWrapper:
|
|||
"""
|
||||
self.logging_loop = loop
|
||||
|
||||
async def _call_post_streaming_deployment_hook(self, chunk):
|
||||
"""
|
||||
Call the post-call streaming deployment hook for callbacks.
|
||||
|
||||
This allows callbacks to modify streaming chunks before they're returned.
|
||||
"""
|
||||
try:
|
||||
import litellm
|
||||
from litellm.integrations.custom_logger import CustomLogger
|
||||
from litellm.types.utils import CallTypes
|
||||
|
||||
# Get request kwargs from logging object
|
||||
request_data = self.logging_obj.model_call_details
|
||||
call_type_str = self.logging_obj.call_type
|
||||
|
||||
try:
|
||||
typed_call_type = CallTypes(call_type_str)
|
||||
except ValueError:
|
||||
typed_call_type = None
|
||||
|
||||
# Call hooks for all callbacks
|
||||
for callback in litellm.callbacks:
|
||||
if isinstance(callback, CustomLogger) and hasattr(callback, "async_post_call_streaming_deployment_hook"):
|
||||
result = await callback.async_post_call_streaming_deployment_hook(
|
||||
request_data=request_data,
|
||||
response_chunk=chunk,
|
||||
call_type=typed_call_type,
|
||||
)
|
||||
if result is not None:
|
||||
chunk = result
|
||||
|
||||
return chunk
|
||||
except Exception as e:
|
||||
from litellm._logging import verbose_logger
|
||||
verbose_logger.exception(f"Error in post-call streaming deployment hook: {str(e)}")
|
||||
return chunk
|
||||
|
||||
def cache_streaming_response(self, processed_chunk, cache_hit: bool):
|
||||
"""
|
||||
Caches the streaming response
|
||||
|
|
@ -1825,6 +1864,11 @@ class CustomStreamWrapper:
|
|||
if self.sent_last_chunk is True and self.stream_options is None:
|
||||
usage = calculate_total_usage(chunks=self.chunks)
|
||||
processed_chunk._hidden_params["usage"] = usage
|
||||
|
||||
# Call post-call streaming deployment hook for final chunk
|
||||
if self.sent_last_chunk is True:
|
||||
processed_chunk = await self._call_post_streaming_deployment_hook(processed_chunk)
|
||||
|
||||
return processed_chunk
|
||||
raise StopAsyncIteration
|
||||
else: # temporary patch for non-aiohttp async calls
|
||||
|
|
|
|||
|
|
@ -31,7 +31,7 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper):
|
|||
sent_first_chunk: bool = False
|
||||
sent_content_block_start: bool = False
|
||||
sent_content_block_finish: bool = False
|
||||
current_content_block_type: Literal["text", "tool_use"] = "text"
|
||||
current_content_block_type: Literal["text", "tool_use", "thinking"] = "text"
|
||||
sent_last_message: bool = False
|
||||
holding_chunk: Optional[Any] = None
|
||||
holding_stop_reason_chunk: Optional[Any] = None
|
||||
|
|
|
|||
|
|
@ -13,6 +13,8 @@ from typing import (
|
|||
|
||||
from openai.types.chat.chat_completion_chunk import Choice as OpenAIStreamingChoice
|
||||
|
||||
from litellm.types.utils import StreamingChoices
|
||||
|
||||
from litellm.types.llms.anthropic import (
|
||||
AllAnthropicToolsValues,
|
||||
AnthopicMessagesAssistantMessageParam,
|
||||
|
|
@ -21,10 +23,14 @@ from litellm.types.llms.anthropic import (
|
|||
AnthropicMessagesToolChoice,
|
||||
AnthropicMessagesUserMessageParam,
|
||||
AnthropicResponseContentBlockText,
|
||||
AnthropicResponseContentBlockRedactedThinking,
|
||||
AnthropicResponseContentBlockThinking,
|
||||
AnthropicResponseContentBlockToolUse,
|
||||
ContentBlockDelta,
|
||||
ContentJsonBlockDelta,
|
||||
ContentTextBlockDelta,
|
||||
ContentThinkingBlockDelta,
|
||||
ContentThinkingSignatureBlockDelta,
|
||||
MessageBlockDelta,
|
||||
MessageDelta,
|
||||
UsageDelta,
|
||||
|
|
@ -39,9 +45,11 @@ from litellm.types.llms.openai import (
|
|||
ChatCompletionAssistantToolCall,
|
||||
ChatCompletionImageObject,
|
||||
ChatCompletionImageUrlObject,
|
||||
ChatCompletionRedactedThinkingBlock,
|
||||
ChatCompletionRequest,
|
||||
ChatCompletionSystemMessage,
|
||||
ChatCompletionTextObject,
|
||||
ChatCompletionThinkingBlock,
|
||||
ChatCompletionToolCallFunctionChunk,
|
||||
ChatCompletionToolChoiceFunctionParam,
|
||||
ChatCompletionToolChoiceObjectParam,
|
||||
|
|
@ -103,7 +111,6 @@ class AnthropicAdapter:
|
|||
def translate_completion_output_params(
|
||||
self, response: ModelResponse
|
||||
) -> Optional[AnthropicMessagesResponse]:
|
||||
|
||||
return LiteLLMAnthropicMessagesAdapter().translate_openai_response_to_anthropic(
|
||||
response=response
|
||||
)
|
||||
|
|
@ -227,6 +234,7 @@ class LiteLLMAnthropicMessagesAdapter:
|
|||
## ASSISTANT MESSAGE ##
|
||||
assistant_message_str: Optional[str] = None
|
||||
tool_calls: List[ChatCompletionAssistantToolCall] = []
|
||||
thinking_blocks: List[Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]] = []
|
||||
if m["role"] == "assistant":
|
||||
if isinstance(m.get("content"), str):
|
||||
assistant_message_str = str(m.get("content", ""))
|
||||
|
|
@ -253,14 +261,33 @@ class LiteLLMAnthropicMessagesAdapter:
|
|||
function=function_chunk,
|
||||
)
|
||||
)
|
||||
elif content.get("type") == "thinking":
|
||||
thinking_block = ChatCompletionThinkingBlock(
|
||||
type="thinking",
|
||||
thinking=content.get("thinking") or "",
|
||||
signature=content.get("signature") or "",
|
||||
cache_control=content.get("cache_control", {})
|
||||
)
|
||||
thinking_blocks.append(thinking_block)
|
||||
elif content.get("type") == "redacted_thinking":
|
||||
redacted_thinking_block = ChatCompletionRedactedThinkingBlock(
|
||||
type="redacted_thinking",
|
||||
data=content.get("data") or "",
|
||||
cache_control=content.get("cache_control", {})
|
||||
)
|
||||
thinking_blocks.append(redacted_thinking_block)
|
||||
|
||||
if assistant_message_str is not None or len(tool_calls) > 0:
|
||||
|
||||
if assistant_message_str is not None or len(tool_calls) > 0 or len(thinking_blocks) > 0:
|
||||
assistant_message = ChatCompletionAssistantMessage(
|
||||
role="assistant",
|
||||
content=assistant_message_str,
|
||||
thinking_blocks=thinking_blocks if len(thinking_blocks) > 0 else None,
|
||||
)
|
||||
if len(tool_calls) > 0:
|
||||
assistant_message["tool_calls"] = tool_calls
|
||||
if len(thinking_blocks) > 0:
|
||||
assistant_message["thinking_blocks"] = thinking_blocks # type: ignore
|
||||
new_messages.append(assistant_message)
|
||||
|
||||
return new_messages
|
||||
|
|
@ -313,6 +340,7 @@ class LiteLLMAnthropicMessagesAdapter:
|
|||
"""
|
||||
This is used by the beta Anthropic Adapter, for translating anthropic `/v1/messages` requests to the openai format.
|
||||
"""
|
||||
# Debug: Processing Anthropic message request
|
||||
new_messages: List[AllMessageValues] = []
|
||||
|
||||
## CONVERT ANTHROPIC MESSAGES TO OPENAI
|
||||
|
|
@ -383,14 +411,37 @@ class LiteLLMAnthropicMessagesAdapter:
|
|||
def _translate_openai_content_to_anthropic(
|
||||
self, choices: List[Choices]
|
||||
) -> List[
|
||||
Union[AnthropicResponseContentBlockText, AnthropicResponseContentBlockToolUse]
|
||||
Union[AnthropicResponseContentBlockText, AnthropicResponseContentBlockToolUse, AnthropicResponseContentBlockThinking, AnthropicResponseContentBlockRedactedThinking]
|
||||
]:
|
||||
new_content: List[
|
||||
Union[
|
||||
AnthropicResponseContentBlockText, AnthropicResponseContentBlockToolUse
|
||||
AnthropicResponseContentBlockText, AnthropicResponseContentBlockToolUse, AnthropicResponseContentBlockThinking, AnthropicResponseContentBlockRedactedThinking
|
||||
]
|
||||
] = []
|
||||
for choice in choices:
|
||||
# Handle thinking blocks first
|
||||
if hasattr(choice.message, 'thinking_blocks') and choice.message.thinking_blocks:
|
||||
for thinking_block in choice.message.thinking_blocks:
|
||||
if thinking_block.get("type") == "thinking":
|
||||
thinking_value = thinking_block.get("thinking", "")
|
||||
signature_value = thinking_block.get("signature", "")
|
||||
new_content.append(
|
||||
AnthropicResponseContentBlockThinking(
|
||||
type="thinking",
|
||||
thinking=str(thinking_value) if thinking_value is not None else "",
|
||||
signature=str(signature_value) if signature_value is not None else None,
|
||||
)
|
||||
)
|
||||
elif thinking_block.get("type") == "redacted_thinking":
|
||||
data_value = thinking_block.get("data", "")
|
||||
new_content.append(
|
||||
AnthropicResponseContentBlockRedactedThinking(
|
||||
type="redacted_thinking",
|
||||
data=str(data_value) if data_value is not None else "",
|
||||
)
|
||||
)
|
||||
|
||||
# Handle tool calls
|
||||
if (
|
||||
choice.message.tool_calls is not None
|
||||
and len(choice.message.tool_calls) > 0
|
||||
|
|
@ -404,12 +455,41 @@ class LiteLLMAnthropicMessagesAdapter:
|
|||
input=json.loads(tool_call.function.arguments) if tool_call.function.arguments else {},
|
||||
)
|
||||
)
|
||||
# Handle text content
|
||||
elif choice.message.content is not None:
|
||||
new_content.append(
|
||||
AnthropicResponseContentBlockText(
|
||||
type="text", text=choice.message.content
|
||||
)
|
||||
)
|
||||
elif choice.message.thinking_blocks is not None:
|
||||
for thinking_block in choice.message.thinking_blocks:
|
||||
if "thinking" in thinking_block and "signature" in thinking_block:
|
||||
thinking = thinking_block.get("thinking")
|
||||
signature = thinking_block.get("signature")
|
||||
|
||||
assert isinstance(thinking, str)
|
||||
assert isinstance(signature, str) or signature is None
|
||||
|
||||
new_content.append(
|
||||
AnthropicResponseContentBlockThinking(
|
||||
type="thinking",
|
||||
thinking=thinking,
|
||||
signature=signature,
|
||||
)
|
||||
)
|
||||
elif "data" in thinking_block:
|
||||
data = thinking_block.get("data")
|
||||
|
||||
assert isinstance(data, str)
|
||||
|
||||
new_content.append(
|
||||
AnthropicResponseContentBlockRedactedThinking(
|
||||
type="redacted_thinking",
|
||||
data=data,
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
return new_content
|
||||
|
||||
|
|
@ -453,9 +533,9 @@ class LiteLLMAnthropicMessagesAdapter:
|
|||
return translated_obj
|
||||
|
||||
def _translate_streaming_openai_chunk_to_anthropic_content_block(
|
||||
self, choices: List[OpenAIStreamingChoice]
|
||||
self, choices: List[Union[OpenAIStreamingChoice, StreamingChoices]]
|
||||
) -> Tuple[
|
||||
Literal["text", "tool_use"],
|
||||
Literal["text", "tool_use", "thinking"],
|
||||
"ContentBlockContentBlockDict",
|
||||
]:
|
||||
from litellm._uuid import uuid
|
||||
|
|
@ -476,17 +556,41 @@ class LiteLLMAnthropicMessagesAdapter:
|
|||
name=choice.delta.tool_calls[0].function.name or "",
|
||||
input={},
|
||||
)
|
||||
elif (
|
||||
isinstance(choice, StreamingChoices) and hasattr(choice.delta, "thinking_blocks")
|
||||
):
|
||||
thinking_blocks = choice.delta.thinking_blocks or []
|
||||
if len(thinking_blocks) > 0:
|
||||
thinking_block = thinking_blocks[0]
|
||||
if thinking_block["type"] == "thinking":
|
||||
thinking = thinking_block.get("thinking") or ""
|
||||
signature = thinking_block.get("signature") or ""
|
||||
|
||||
assert isinstance(thinking, str)
|
||||
assert isinstance(signature, str)
|
||||
|
||||
if thinking and signature:
|
||||
raise ValueError("Both `thinking` and `signature` in a single streaming chunk isn't supported.")
|
||||
|
||||
return "thinking", ChatCompletionThinkingBlock(
|
||||
type="thinking",
|
||||
thinking=thinking,
|
||||
signature=signature
|
||||
)
|
||||
|
||||
|
||||
return "text", TextBlock(type="text", text="")
|
||||
|
||||
def _translate_streaming_openai_chunk_to_anthropic(
|
||||
self, choices: List[OpenAIStreamingChoice]
|
||||
self, choices: List[Union[OpenAIStreamingChoice, StreamingChoices]]
|
||||
) -> Tuple[
|
||||
Literal["text_delta", "input_json_delta"],
|
||||
Union[ContentTextBlockDelta, ContentJsonBlockDelta],
|
||||
Literal["text_delta", "input_json_delta", "thinking_delta", "signature_delta"],
|
||||
Union[ContentTextBlockDelta, ContentJsonBlockDelta, ContentThinkingBlockDelta, ContentThinkingSignatureBlockDelta],
|
||||
]:
|
||||
|
||||
text: str = ""
|
||||
reasoning_content: str = ""
|
||||
reasoning_signature: str = ""
|
||||
partial_json: Optional[str] = None
|
||||
for choice in choices:
|
||||
if choice.delta.content is not None and len(choice.delta.content) > 0:
|
||||
|
|
@ -499,10 +603,32 @@ class LiteLLMAnthropicMessagesAdapter:
|
|||
and tool.function.arguments is not None
|
||||
):
|
||||
partial_json += tool.function.arguments
|
||||
elif isinstance(choice, StreamingChoices) and hasattr(choice.delta, "thinking_blocks"):
|
||||
thinking_blocks = choice.delta.thinking_blocks or []
|
||||
if len(thinking_blocks) > 0:
|
||||
for thinking_block in thinking_blocks:
|
||||
if thinking_block["type"] == "thinking":
|
||||
thinking = thinking_block.get("thinking") or ""
|
||||
signature = thinking_block.get("signature") or ""
|
||||
|
||||
assert isinstance(thinking, str)
|
||||
assert isinstance(signature, str)
|
||||
|
||||
reasoning_content += thinking
|
||||
reasoning_signature += signature
|
||||
|
||||
if reasoning_content and reasoning_signature:
|
||||
raise ValueError("Both `reasoning` and `signature` in a single streaming chunk isn't supported.")
|
||||
|
||||
|
||||
if partial_json is not None:
|
||||
return "input_json_delta", ContentJsonBlockDelta(
|
||||
type="input_json_delta", partial_json=partial_json
|
||||
)
|
||||
elif reasoning_content:
|
||||
return "thinking_delta", ContentThinkingBlockDelta(type="thinking_delta", thinking=reasoning_content)
|
||||
elif reasoning_signature:
|
||||
return "signature_delta", ContentThinkingSignatureBlockDelta(type="signature_delta", signature=reasoning_signature)
|
||||
else:
|
||||
return "text_delta", ContentTextBlockDelta(type="text_delta", text=text)
|
||||
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@ from typing import Any, AsyncIterator, Dict, List, Optional, Tuple
|
|||
|
||||
import httpx
|
||||
|
||||
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
|
||||
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj, verbose_logger
|
||||
from litellm.llms.base_llm.anthropic_messages.transformation import (
|
||||
BaseAnthropicMessagesConfig,
|
||||
)
|
||||
|
|
@ -94,6 +94,7 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
|
|||
status_code=400,
|
||||
)
|
||||
####### get required params for all anthropic messages requests ######
|
||||
verbose_logger.info(f"🔍 TRANSFORMATION DEBUG - Messages: {messages}")
|
||||
anthropic_messages_request: AnthropicMessagesRequest = AnthropicMessagesRequest(
|
||||
messages=messages,
|
||||
max_tokens=max_tokens,
|
||||
|
|
|
|||
|
|
@ -984,11 +984,20 @@ class BaseAWSLLM:
|
|||
raise ImportError(
|
||||
"Missing boto3 to call bedrock. Run 'pip install boto3'."
|
||||
)
|
||||
|
||||
# Filter headers for AWS signature calculation
|
||||
# AWS SigV4 only includes specific headers in signature calculation
|
||||
aws_signature_headers = self._filter_headers_for_aws_signature(headers)
|
||||
sigv4 = SigV4Auth(credentials, "bedrock", aws_region_name)
|
||||
request = AWSRequest(
|
||||
method="POST", url=endpoint_url, data=data, headers=headers
|
||||
method="POST", url=endpoint_url, data=data, headers=aws_signature_headers
|
||||
)
|
||||
sigv4.add_auth(request)
|
||||
|
||||
# Add back all original headers (including forwarded ones) after signature calculation
|
||||
for header_name, header_value in headers.items():
|
||||
request.headers[header_name] = header_value
|
||||
|
||||
if (
|
||||
extra_headers is not None and "Authorization" in extra_headers
|
||||
): # prevent sigv4 from overwriting the auth header
|
||||
|
|
@ -997,6 +1006,25 @@ class BaseAWSLLM:
|
|||
|
||||
return prepped
|
||||
|
||||
def _filter_headers_for_aws_signature(self, headers: dict) -> dict:
|
||||
"""
|
||||
Filter headers to only include those that AWS SigV4 includes in signature calculation.
|
||||
This Fixes forwarded client headers from breaking the signature calculation.
|
||||
"""
|
||||
aws_signature_headers = {}
|
||||
aws_headers = {
|
||||
'host', 'content-type', 'date', 'x-amz-date', 'x-amz-security-token',
|
||||
'x-amz-content-sha256', 'x-amz-algorithm', 'x-amz-credential',
|
||||
'x-amz-signedheaders', 'x-amz-signature'
|
||||
}
|
||||
|
||||
for header_name, header_value in headers.items():
|
||||
header_lower = header_name.lower()
|
||||
if header_lower in aws_headers or header_lower.startswith('x-amz-') or header_lower.startswith('x-amzn-'):
|
||||
aws_signature_headers[header_name] = header_value
|
||||
|
||||
return aws_signature_headers
|
||||
|
||||
def _sign_request(
|
||||
self,
|
||||
service_name: Literal["bedrock", "sagemaker"],
|
||||
|
|
|
|||
|
|
@ -111,7 +111,6 @@ class AmazonAnthropicClaudeMessagesConfig(
|
|||
litellm_params=litellm_params,
|
||||
headers=headers,
|
||||
)
|
||||
|
||||
#########################################################
|
||||
############## BEDROCK Invoke SPECIFIC TRANSFORMATION ###
|
||||
#########################################################
|
||||
|
|
|
|||
|
|
@ -317,5 +317,20 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
|
|||
)
|
||||
|
||||
logging_obj.model_call_details["httpx_response"] = raw_response
|
||||
response = self.convert_citation_sources_to_citations(response)
|
||||
|
||||
return GenerateContentResponse(**response)
|
||||
|
||||
def convert_citation_sources_to_citations(self, response: Dict) -> Dict:
|
||||
"""
|
||||
Convert citation sources to citations.
|
||||
API's camelCase citationSources becomes the SDK's snake_case citations
|
||||
"""
|
||||
if "candidates" in response:
|
||||
for candidate in response["candidates"]:
|
||||
if "citationMetadata" in candidate and isinstance(candidate["citationMetadata"], dict):
|
||||
citation_metadata = candidate["citationMetadata"]
|
||||
# Transform citationSources to citations to match expected schema
|
||||
if "citationSources" in citation_metadata:
|
||||
citation_metadata["citations"] = citation_metadata.pop("citationSources")
|
||||
return response
|
||||
|
|
@ -17,12 +17,12 @@ from litellm.utils import (
|
|||
CustomStreamWrapper,
|
||||
EmbeddingResponse,
|
||||
ModelResponse,
|
||||
Usage,
|
||||
get_secret,
|
||||
)
|
||||
|
||||
from ..common_utils import AWSEventStreamDecoder, SagemakerError
|
||||
from .transformation import SagemakerConfig
|
||||
from ..embedding.transformation import SagemakerEmbeddingConfig
|
||||
|
||||
sagemaker_config = SagemakerConfig()
|
||||
|
||||
|
|
@ -578,7 +578,7 @@ class SagemakerLLM(BaseAWSLLM):
|
|||
logger_fn=None,
|
||||
):
|
||||
"""
|
||||
Supports Huggingface Jumpstart embeddings like GPT-6B
|
||||
Supports both Huggingface Jumpstart embeddings and Voyage models
|
||||
"""
|
||||
### BOTO3 INIT
|
||||
import boto3
|
||||
|
|
@ -625,8 +625,11 @@ class SagemakerLLM(BaseAWSLLM):
|
|||
): # completion(top_k=3) > sagemaker_config(top_k=3) <- allows for dynamic variables to be passed in
|
||||
inference_params[k] = v
|
||||
|
||||
#### HF EMBEDDING LOGIC
|
||||
data = json.dumps({"inputs": input}).encode("utf-8")
|
||||
#### EMBEDDING LOGIC
|
||||
# Transform request based on model type
|
||||
provider_config = SagemakerEmbeddingConfig.get_model_config(model)
|
||||
request_data = provider_config.transform_embedding_request(model, input, optional_params, {})
|
||||
data = json.dumps(request_data).encode("utf-8")
|
||||
|
||||
## LOGGING
|
||||
request_str = f"""
|
||||
|
|
@ -670,40 +673,27 @@ class SagemakerLLM(BaseAWSLLM):
|
|||
)
|
||||
|
||||
print_verbose(f"raw model_response: {response}")
|
||||
if "embedding" not in response:
|
||||
raise SagemakerError(
|
||||
status_code=500, message="embedding not found in response"
|
||||
)
|
||||
embeddings = response["embedding"]
|
||||
|
||||
if not isinstance(embeddings, list):
|
||||
raise SagemakerError(
|
||||
status_code=422,
|
||||
message=f"Response not in expected format - {embeddings}",
|
||||
)
|
||||
|
||||
output_data = []
|
||||
for idx, embedding in enumerate(embeddings):
|
||||
output_data.append(
|
||||
{"object": "embedding", "index": idx, "embedding": embedding}
|
||||
)
|
||||
|
||||
model_response.object = "list"
|
||||
model_response.data = output_data
|
||||
model_response.model = model
|
||||
|
||||
input_tokens = 0
|
||||
for text in input:
|
||||
input_tokens += len(encoding.encode(text))
|
||||
|
||||
setattr(
|
||||
model_response,
|
||||
"usage",
|
||||
Usage(
|
||||
prompt_tokens=input_tokens,
|
||||
completion_tokens=0,
|
||||
total_tokens=input_tokens,
|
||||
),
|
||||
|
||||
# Transform response based on model type
|
||||
from httpx import Response as HttpxResponse
|
||||
|
||||
# Create a mock httpx Response object for the transformation
|
||||
mock_response = HttpxResponse(
|
||||
status_code=200,
|
||||
content=json.dumps(response).encode('utf-8'),
|
||||
headers={"content-type": "application/json"}
|
||||
)
|
||||
|
||||
model_response = EmbeddingResponse()
|
||||
|
||||
# Use the request_data that was already transformed above
|
||||
return provider_config.transform_embedding_response(
|
||||
model=model,
|
||||
raw_response=mock_response,
|
||||
model_response=model_response,
|
||||
logging_obj=logging_obj,
|
||||
api_key=None,
|
||||
request_data=request_data,
|
||||
optional_params=optional_params,
|
||||
litellm_params=litellm_params or {}
|
||||
)
|
||||
|
||||
return model_response
|
||||
|
|
|
|||
|
|
@ -8,6 +8,7 @@ import json
|
|||
import time
|
||||
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
|
||||
|
||||
|
||||
from httpx._models import Headers, Response
|
||||
|
||||
import litellm
|
||||
|
|
@ -277,3 +278,5 @@ class SagemakerConfig(BaseConfig):
|
|||
headers = {"Content-Type": "application/json", **headers}
|
||||
|
||||
return headers
|
||||
|
||||
|
||||
|
|
|
|||
154
litellm/llms/sagemaker/embedding/transformation.py
Normal file
154
litellm/llms/sagemaker/embedding/transformation.py
Normal file
|
|
@ -0,0 +1,154 @@
|
|||
"""
|
||||
Translate from OpenAI's `/v1/embeddings` to Sagemaker's `/invoke`
|
||||
|
||||
In the Huggingface TGI format.
|
||||
"""
|
||||
|
||||
from typing import TYPE_CHECKING, Any, List, Optional, Union
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from litellm.types.llms.openai import AllEmbeddingInputValues
|
||||
|
||||
from httpx._models import Headers, Response
|
||||
|
||||
from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig
|
||||
from litellm.llms.base_llm.chat.transformation import BaseLLMException
|
||||
from litellm.types.utils import Usage, EmbeddingResponse
|
||||
from litellm.llms.voyage.embedding.transformation import VoyageEmbeddingConfig
|
||||
|
||||
from ..common_utils import SagemakerError
|
||||
|
||||
|
||||
class SagemakerEmbeddingConfig(BaseEmbeddingConfig):
|
||||
"""
|
||||
SageMaker embedding configuration factory for supporting embedding parameters
|
||||
"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
pass
|
||||
|
||||
@classmethod
|
||||
def get_model_config(cls, model: str) -> "BaseEmbeddingConfig":
|
||||
"""
|
||||
Factory method to get the appropriate embedding config based on model type
|
||||
|
||||
Args:
|
||||
model: The model name
|
||||
|
||||
Returns:
|
||||
Appropriate embedding config instance
|
||||
"""
|
||||
if "voyage" in model.lower():
|
||||
return VoyageEmbeddingConfig()
|
||||
else:
|
||||
return cls()
|
||||
|
||||
def get_supported_openai_params(self, model: str) -> List[str]:
|
||||
# Check if this is an embedding model
|
||||
if "voyage" in model.lower():
|
||||
return VoyageEmbeddingConfig().get_supported_openai_params(model)
|
||||
else:
|
||||
return []
|
||||
|
||||
def map_openai_params(
|
||||
self,
|
||||
non_default_params: dict,
|
||||
optional_params: dict,
|
||||
model: str,
|
||||
drop_params: bool,
|
||||
) -> dict:
|
||||
|
||||
return optional_params
|
||||
|
||||
def get_error_class(
|
||||
self, error_message: str, status_code: int, headers: Union[dict, Headers]
|
||||
) -> BaseLLMException:
|
||||
return SagemakerError(
|
||||
message=error_message, status_code=status_code, headers=headers
|
||||
)
|
||||
|
||||
def transform_embedding_request(
|
||||
self,
|
||||
model: str,
|
||||
input: "AllEmbeddingInputValues",
|
||||
optional_params: dict,
|
||||
headers: dict,
|
||||
) -> dict:
|
||||
"""
|
||||
Transform embedding request for Hugging Face models on SageMaker
|
||||
"""
|
||||
# HF models expect "inputs" field (plural)
|
||||
return {"inputs": input, **optional_params}
|
||||
|
||||
def transform_embedding_response(
|
||||
self,
|
||||
model: str,
|
||||
raw_response: Response,
|
||||
model_response: "EmbeddingResponse",
|
||||
logging_obj: Any,
|
||||
api_key: Optional[str] = None,
|
||||
request_data: dict = {},
|
||||
optional_params: dict = {},
|
||||
litellm_params: dict = {},
|
||||
) -> "EmbeddingResponse":
|
||||
"""
|
||||
Transform embedding response for Hugging Face models on SageMaker
|
||||
"""
|
||||
try:
|
||||
response_data = raw_response.json()
|
||||
except Exception as e:
|
||||
raise SagemakerError(
|
||||
message=f"Failed to parse response: {str(e)}",
|
||||
status_code=raw_response.status_code
|
||||
)
|
||||
|
||||
if "embedding" not in response_data:
|
||||
raise SagemakerError(
|
||||
status_code=500, message="HF response missing 'embedding' field"
|
||||
)
|
||||
embeddings = response_data["embedding"]
|
||||
|
||||
if not isinstance(embeddings, list):
|
||||
raise SagemakerError(
|
||||
status_code=422,
|
||||
message=f"HF response not in expected format - {embeddings}",
|
||||
)
|
||||
|
||||
output_data = []
|
||||
for idx, embedding in enumerate(embeddings):
|
||||
output_data.append(
|
||||
{"object": "embedding", "index": idx, "embedding": embedding}
|
||||
)
|
||||
|
||||
model_response.object = "list"
|
||||
model_response.data = output_data
|
||||
model_response.model = model
|
||||
|
||||
# Calculate usage from request data
|
||||
input_texts = request_data.get("inputs", [])
|
||||
input_tokens = 0
|
||||
for text in input_texts:
|
||||
input_tokens += len(text.split()) # Simple word count fallback
|
||||
|
||||
model_response.usage = Usage(
|
||||
prompt_tokens=input_tokens,
|
||||
completion_tokens=0,
|
||||
total_tokens=input_tokens,
|
||||
)
|
||||
|
||||
return model_response
|
||||
|
||||
def validate_environment(
|
||||
self,
|
||||
headers: dict,
|
||||
model: str,
|
||||
messages: List[Any],
|
||||
optional_params: dict,
|
||||
litellm_params: dict,
|
||||
api_key: Optional[str] = None,
|
||||
api_base: Optional[str] = None,
|
||||
) -> dict:
|
||||
"""
|
||||
Validate environment for SageMaker embeddings
|
||||
"""
|
||||
return {"Content-Type": "application/json"}
|
||||
|
|
@ -302,26 +302,27 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915
|
|||
)
|
||||
if thinking_blocks is not None:
|
||||
for block in thinking_blocks:
|
||||
block_thinking_str = block.get("thinking")
|
||||
block_signature = block.get("signature")
|
||||
if (
|
||||
block_thinking_str is not None
|
||||
and block_signature is not None
|
||||
):
|
||||
try:
|
||||
assistant_content.append(
|
||||
PartType(
|
||||
thoughtSignature=block_signature,
|
||||
**json.loads(block_thinking_str),
|
||||
if block["type"] == "thinking":
|
||||
block_thinking_str = block.get("thinking")
|
||||
block_signature = block.get("signature")
|
||||
if (
|
||||
block_thinking_str is not None
|
||||
and block_signature is not None
|
||||
):
|
||||
try:
|
||||
assistant_content.append(
|
||||
PartType(
|
||||
thoughtSignature=block_signature,
|
||||
**json.loads(block_thinking_str),
|
||||
)
|
||||
)
|
||||
)
|
||||
except Exception:
|
||||
assistant_content.append(
|
||||
PartType(
|
||||
thoughtSignature=block_signature,
|
||||
text=block_thinking_str,
|
||||
except Exception:
|
||||
assistant_content.append(
|
||||
PartType(
|
||||
thoughtSignature=block_signature,
|
||||
text=block_thinking_str,
|
||||
)
|
||||
)
|
||||
)
|
||||
if _message_content is not None and isinstance(_message_content, list):
|
||||
_parts = []
|
||||
for element in _message_content:
|
||||
|
|
|
|||
|
|
@ -215,6 +215,15 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
|
|||
@classmethod
|
||||
def get_config(cls):
|
||||
return super().get_config()
|
||||
|
||||
def _supports_penalty_parameters(self, model: str) -> bool:
|
||||
unsupported_models = ["gemini-2.5-pro-preview-06-05"]
|
||||
|
||||
for pattern in unsupported_models:
|
||||
if model in pattern:
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def get_supported_openai_params(self, model: str) -> List[str]:
|
||||
supported_params = [
|
||||
|
|
@ -229,8 +238,6 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
|
|||
"response_format",
|
||||
"n",
|
||||
"stop",
|
||||
"frequency_penalty",
|
||||
"presence_penalty",
|
||||
"extra_headers",
|
||||
"seed",
|
||||
"logprobs",
|
||||
|
|
@ -239,6 +246,11 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
|
|||
"parallel_tool_calls",
|
||||
"web_search_options",
|
||||
]
|
||||
|
||||
# Add penalty parameters only for non-preview models
|
||||
if self._supports_penalty_parameters(model):
|
||||
supported_params.extend(["frequency_penalty", "presence_penalty"])
|
||||
|
||||
if supports_reasoning(model):
|
||||
supported_params.append("reasoning_effort")
|
||||
supported_params.append("thinking")
|
||||
|
|
@ -680,8 +692,12 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
|
|||
)
|
||||
elif param == "frequency_penalty":
|
||||
optional_params["frequency_penalty"] = value
|
||||
if self._supports_penalty_parameters(model):
|
||||
optional_params["frequency_penalty"] = value
|
||||
elif param == "presence_penalty":
|
||||
optional_params["presence_penalty"] = value
|
||||
if self._supports_penalty_parameters(model):
|
||||
optional_params["presence_penalty"] = value
|
||||
elif param == "logprobs":
|
||||
optional_params["responseLogprobs"] = value
|
||||
elif param == "top_logprobs":
|
||||
|
|
|
|||
5
litellm/llms/vertex_ai/rerank/handler.py
Normal file
5
litellm/llms/vertex_ai/rerank/handler.py
Normal file
|
|
@ -0,0 +1,5 @@
|
|||
"""
|
||||
Vertex AI Rerank - uses `llm_http_handler.py` to make httpx requests
|
||||
|
||||
Request/Response transformation is handled in `transformation.py`
|
||||
"""
|
||||
227
litellm/llms/vertex_ai/rerank/transformation.py
Normal file
227
litellm/llms/vertex_ai/rerank/transformation.py
Normal file
|
|
@ -0,0 +1,227 @@
|
|||
"""
|
||||
Translates from Cohere's `/v1/rerank` input format to Vertex AI Discovery Engine's `/rank` input format.
|
||||
|
||||
Why separate file? Make it easy to see how transformation works
|
||||
"""
|
||||
|
||||
from typing import Any, Dict, List, Optional, Union
|
||||
|
||||
import httpx
|
||||
|
||||
import litellm
|
||||
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
|
||||
from litellm.llms.base_llm.rerank.transformation import BaseRerankConfig
|
||||
from litellm.llms.vertex_ai.vertex_llm_base import VertexBase
|
||||
from litellm.secret_managers.main import get_secret_str
|
||||
from litellm.types.rerank import RerankResponse, RerankResponseMeta, RerankBilledUnits, RerankResponseResult
|
||||
|
||||
|
||||
|
||||
class VertexAIRerankConfig(BaseRerankConfig, VertexBase):
|
||||
"""
|
||||
Configuration for Vertex AI Discovery Engine Rerank API
|
||||
|
||||
Reference: https://cloud.google.com/generative-ai-app-builder/docs/ranking#rank_or_rerank_a_set_of_records_according_to_a_query
|
||||
"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
super().__init__()
|
||||
|
||||
def get_complete_url(self, api_base: Optional[str], model: str) -> str:
|
||||
"""
|
||||
Get the complete URL for the Vertex AI Discovery Engine ranking API
|
||||
"""
|
||||
# Get project ID from environment or litellm config
|
||||
project_id = (
|
||||
get_secret_str("VERTEXAI_PROJECT")
|
||||
or litellm.vertex_project
|
||||
)
|
||||
|
||||
if not project_id:
|
||||
raise ValueError(
|
||||
"Vertex AI project ID is required. Please set 'VERTEXAI_PROJECT' or 'litellm.vertex_project'"
|
||||
)
|
||||
|
||||
return f"https://discoveryengine.googleapis.com/v1/projects/{project_id}/locations/global/rankingConfigs/default_ranking_config:rank"
|
||||
|
||||
def validate_environment(
|
||||
self,
|
||||
headers: dict,
|
||||
model: str,
|
||||
api_key: Optional[str] = None,
|
||||
) -> dict:
|
||||
"""
|
||||
Validate and set up authentication for Vertex AI Discovery Engine API
|
||||
"""
|
||||
# Get credentials and project info
|
||||
vertex_credentials = self.get_vertex_ai_credentials({})
|
||||
vertex_project = self.get_vertex_ai_project({})
|
||||
|
||||
# Get access token using the base class method
|
||||
access_token, project_id = self._ensure_access_token(
|
||||
credentials=vertex_credentials,
|
||||
project_id=vertex_project,
|
||||
custom_llm_provider="vertex_ai",
|
||||
)
|
||||
|
||||
default_headers = {
|
||||
"Authorization": f"Bearer {access_token}",
|
||||
"Content-Type": "application/json",
|
||||
"X-Goog-User-Project": project_id,
|
||||
}
|
||||
|
||||
# If 'Authorization' is provided in headers, it overrides the default.
|
||||
if "Authorization" in headers:
|
||||
default_headers["Authorization"] = headers["Authorization"]
|
||||
|
||||
# Merge other headers, overriding any default ones except Authorization
|
||||
return {**default_headers, **headers}
|
||||
|
||||
def transform_rerank_request(
|
||||
self,
|
||||
model: str,
|
||||
optional_rerank_params: Dict,
|
||||
headers: dict,
|
||||
) -> dict:
|
||||
"""
|
||||
Transform the request from Cohere format to Vertex AI Discovery Engine format
|
||||
"""
|
||||
if "query" not in optional_rerank_params:
|
||||
raise ValueError("query is required for Vertex AI rerank")
|
||||
if "documents" not in optional_rerank_params:
|
||||
raise ValueError("documents is required for Vertex AI rerank")
|
||||
|
||||
query = optional_rerank_params["query"]
|
||||
documents = optional_rerank_params["documents"]
|
||||
top_n = optional_rerank_params.get("top_n", None)
|
||||
return_documents = optional_rerank_params.get("return_documents", True)
|
||||
|
||||
# Convert documents to records format
|
||||
records = []
|
||||
for idx, document in enumerate(documents):
|
||||
if isinstance(document, str):
|
||||
content = document
|
||||
title = " ".join(document.split()[:3]) # First 3 words as title
|
||||
else:
|
||||
# Handle dict format
|
||||
content = document.get("text", str(document))
|
||||
title = document.get("title", " ".join(content.split()[:3]))
|
||||
|
||||
records.append({
|
||||
"id": str(idx),
|
||||
"title": title,
|
||||
"content": content
|
||||
})
|
||||
|
||||
request_data = {
|
||||
"model": model,
|
||||
"query": query,
|
||||
"records": records
|
||||
}
|
||||
|
||||
if top_n is not None:
|
||||
request_data["topN"] = top_n
|
||||
|
||||
# Map return_documents to ignoreRecordDetailsInResponse
|
||||
# When return_documents is False, we want to ignore record details (return only IDs)
|
||||
request_data["ignoreRecordDetailsInResponse"] = not return_documents
|
||||
|
||||
return request_data
|
||||
|
||||
def transform_rerank_response(
|
||||
self,
|
||||
model: str,
|
||||
raw_response: httpx.Response,
|
||||
model_response: RerankResponse,
|
||||
logging_obj: LiteLLMLoggingObj,
|
||||
api_key: Optional[str] = None,
|
||||
request_data: dict = {},
|
||||
optional_params: dict = {},
|
||||
litellm_params: dict = {},
|
||||
) -> RerankResponse:
|
||||
"""
|
||||
Transform Vertex AI Discovery Engine response to Cohere format
|
||||
"""
|
||||
try:
|
||||
raw_response_json = raw_response.json()
|
||||
except Exception as e:
|
||||
raise ValueError(f"Failed to parse response: {e}")
|
||||
|
||||
# Extract records from response
|
||||
records = raw_response_json.get("records", [])
|
||||
|
||||
# Convert to Cohere format
|
||||
results = []
|
||||
for record in records:
|
||||
# Handle both cases: with full details and with only IDs
|
||||
if "score" in record:
|
||||
# Full response with score and details
|
||||
results.append({
|
||||
"index": int(record["id"]),
|
||||
"relevance_score": record.get("score", 0.0)
|
||||
})
|
||||
else:
|
||||
# Response with only IDs (when ignoreRecordDetailsInResponse=true)
|
||||
# We can't provide a relevance score, so we'll use a default
|
||||
results.append({
|
||||
"index": int(record["id"]),
|
||||
"relevance_score": 1.0 # Default score when details are ignored
|
||||
})
|
||||
|
||||
# Sort by relevance score (descending)
|
||||
results.sort(key=lambda x: x["relevance_score"], reverse=True)
|
||||
|
||||
# Create response in Cohere format
|
||||
# Convert results to proper RerankResponseResult objects
|
||||
rerank_results = []
|
||||
for result in results:
|
||||
rerank_results.append(RerankResponseResult(
|
||||
index=result["index"],
|
||||
relevance_score=result["relevance_score"]
|
||||
))
|
||||
|
||||
# Create meta object
|
||||
meta = RerankResponseMeta(
|
||||
billed_units=RerankBilledUnits(
|
||||
search_units=len(records)
|
||||
)
|
||||
)
|
||||
|
||||
return RerankResponse(
|
||||
id=f"vertex_ai_rerank_{model}",
|
||||
results=rerank_results,
|
||||
meta=meta
|
||||
)
|
||||
|
||||
def get_supported_cohere_rerank_params(self, model: str) -> list:
|
||||
return [
|
||||
"query",
|
||||
"documents",
|
||||
"top_n",
|
||||
"return_documents",
|
||||
]
|
||||
|
||||
def map_cohere_rerank_params(
|
||||
self,
|
||||
non_default_params: dict,
|
||||
model: str,
|
||||
drop_params: bool,
|
||||
query: str,
|
||||
documents: List[Union[str, Dict[str, Any]]],
|
||||
custom_llm_provider: Optional[str] = None,
|
||||
top_n: Optional[int] = None,
|
||||
rank_fields: Optional[List[str]] = None,
|
||||
return_documents: Optional[bool] = True,
|
||||
max_chunks_per_doc: Optional[int] = None,
|
||||
max_tokens_per_doc: Optional[int] = None,
|
||||
) -> Dict:
|
||||
"""
|
||||
Map Cohere rerank params to Vertex AI format
|
||||
"""
|
||||
return {
|
||||
"query": query,
|
||||
"documents": documents,
|
||||
"top_n": top_n,
|
||||
"return_documents": return_documents,
|
||||
}
|
||||
|
||||
|
|
@ -1,17 +1,19 @@
|
|||
"""
|
||||
Helper util for handling XAI-specific cost calculation
|
||||
- e.g.: reasoning tokens for grok models
|
||||
- Uses the generic cost calculator which already handles tiered pricing correctly
|
||||
- Handles XAI-specific reasoning token billing (billed as part of completion tokens)
|
||||
"""
|
||||
|
||||
from typing import Tuple, Union
|
||||
from typing import Tuple
|
||||
|
||||
from litellm.types.utils import Usage
|
||||
from litellm.utils import get_model_info
|
||||
from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token
|
||||
|
||||
|
||||
def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]:
|
||||
"""
|
||||
Calculates the cost per token for a given XAI model, prompt tokens, and completion tokens.
|
||||
Uses the generic cost calculator for all pricing logic, with XAI-specific reasoning token handling.
|
||||
|
||||
Input:
|
||||
- model: str, the model name without provider prefix
|
||||
|
|
@ -20,35 +22,27 @@ def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]:
|
|||
Returns:
|
||||
Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd
|
||||
"""
|
||||
## GET MODEL INFO
|
||||
model_info = get_model_info(model=model, custom_llm_provider="xai")
|
||||
|
||||
def _safe_float_cast(
|
||||
value: Union[str, int, float, None, object], default: float = 0.0
|
||||
) -> float:
|
||||
"""Safely cast a value to float with proper type handling for mypy."""
|
||||
if value is None:
|
||||
return default
|
||||
try:
|
||||
return float(value) # type: ignore
|
||||
except (ValueError, TypeError):
|
||||
return default
|
||||
|
||||
## CALCULATE INPUT COST
|
||||
input_cost_per_token = _safe_float_cast(model_info.get("input_cost_per_token"))
|
||||
prompt_cost: float = (usage.prompt_tokens or 0) * input_cost_per_token
|
||||
|
||||
## CALCULATE OUTPUT COST
|
||||
output_cost_per_token = _safe_float_cast(model_info.get("output_cost_per_token"))
|
||||
|
||||
# XAI-specific completion cost calculation
|
||||
# For XAI models, completion is billed as (visible completion tokens + reasoning tokens)
|
||||
completion_tokens = int(getattr(usage, "completion_tokens", 0) or 0)
|
||||
reasoning_tokens = 0
|
||||
if hasattr(usage, "completion_tokens_details") and usage.completion_tokens_details:
|
||||
reasoning_tokens = int(
|
||||
getattr(usage.completion_tokens_details, "reasoning_tokens", 0) or 0
|
||||
)
|
||||
reasoning_tokens = int(getattr(usage.completion_tokens_details, "reasoning_tokens", 0) or 0)
|
||||
|
||||
completion_cost = (completion_tokens + reasoning_tokens) * output_cost_per_token
|
||||
total_completion_tokens = completion_tokens + reasoning_tokens
|
||||
|
||||
modified_usage = Usage(
|
||||
prompt_tokens=usage.prompt_tokens,
|
||||
completion_tokens=total_completion_tokens,
|
||||
total_tokens=usage.total_tokens,
|
||||
prompt_tokens_details=usage.prompt_tokens_details,
|
||||
completion_tokens_details=None
|
||||
)
|
||||
|
||||
prompt_cost, completion_cost = generic_cost_per_token(
|
||||
model=model,
|
||||
usage=modified_usage,
|
||||
custom_llm_provider="xai"
|
||||
)
|
||||
|
||||
return prompt_cost, completion_cost
|
||||
|
|
|
|||
|
|
@ -12179,6 +12179,44 @@
|
|||
"supports_vision": true,
|
||||
"tool_use_system_prompt_tokens": 159
|
||||
},
|
||||
"global.anthropic.claude-haiku-4-5-20251001-v1:0": {
|
||||
"cache_creation_input_token_cost": 1.25e-06,
|
||||
"cache_read_input_token_cost": 1e-07,
|
||||
"input_cost_per_token": 1e-06,
|
||||
"litellm_provider": "bedrock_converse",
|
||||
"max_input_tokens": 200000,
|
||||
"max_output_tokens": 8192,
|
||||
"max_tokens": 8192,
|
||||
"mode": "chat",
|
||||
"output_cost_per_token": 5e-06,
|
||||
"supports_assistant_prefill": true,
|
||||
"supports_function_calling": true,
|
||||
"supports_pdf_input": true,
|
||||
"supports_prompt_caching": true,
|
||||
"supports_response_schema": true,
|
||||
"supports_tool_choice": true,
|
||||
"supports_vision": true,
|
||||
"tool_use_system_prompt_tokens": 346
|
||||
},
|
||||
"global.anthropic.claude-haiku-4-5-20251001-v1:0": {
|
||||
"cache_creation_input_token_cost": 1.25e-06,
|
||||
"cache_read_input_token_cost": 1e-07,
|
||||
"input_cost_per_token": 1e-06,
|
||||
"litellm_provider": "bedrock_converse",
|
||||
"max_input_tokens": 200000,
|
||||
"max_output_tokens": 8192,
|
||||
"max_tokens": 8192,
|
||||
"mode": "chat",
|
||||
"output_cost_per_token": 5e-06,
|
||||
"supports_assistant_prefill": true,
|
||||
"supports_function_calling": true,
|
||||
"supports_pdf_input": true,
|
||||
"supports_prompt_caching": true,
|
||||
"supports_response_schema": true,
|
||||
"supports_tool_choice": true,
|
||||
"supports_vision": true,
|
||||
"tool_use_system_prompt_tokens": 346
|
||||
},
|
||||
"gpt-3.5-turbo": {
|
||||
"input_cost_per_token": 0.5e-06,
|
||||
"litellm_provider": "openai",
|
||||
|
|
@ -20612,6 +20650,25 @@
|
|||
"supports_vision": true,
|
||||
"tool_use_system_prompt_tokens": 346
|
||||
},
|
||||
"au.anthropic.claude-haiku-4-5-20251001-v1:0": {
|
||||
"cache_creation_input_token_cost": 1.375e-06,
|
||||
"cache_read_input_token_cost": 1.1e-07,
|
||||
"input_cost_per_token": 1.1e-06,
|
||||
"litellm_provider": "bedrock_converse",
|
||||
"max_input_tokens": 200000,
|
||||
"max_output_tokens": 8192,
|
||||
"max_tokens": 8192,
|
||||
"mode": "chat",
|
||||
"output_cost_per_token": 5.5e-06,
|
||||
"supports_assistant_prefill": true,
|
||||
"supports_function_calling": true,
|
||||
"supports_pdf_input": true,
|
||||
"supports_prompt_caching": true,
|
||||
"supports_response_schema": true,
|
||||
"supports_tool_choice": true,
|
||||
"supports_vision": true,
|
||||
"tool_use_system_prompt_tokens": 346
|
||||
},
|
||||
"us.anthropic.claude-opus-4-20250514-v1:0": {
|
||||
"cache_creation_input_token_cost": 1.875e-05,
|
||||
"cache_read_input_token_cost": 1.5e-06,
|
||||
|
|
|
|||
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|
@ -0,0 +1,5 @@
|
|||
<<<<<<<< HEAD:ui/litellm-dashboard/out/_next/static/chunks/app/(dashboard)/experimental/tag-management/page-84e24f0f36de61e2.js
|
||||
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|
||||
========
|
||||
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|
||||
>>>>>>>> ef0d6f0207 (Resolve conflicts in generated HTML files):litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/experimental/tag-management/page-1da0c9dcc6fabf28.js
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||||
|
|
@ -1 +0,0 @@
|
|||
(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[6061],{86947:function(n,u,e){Promise.resolve().then(e.bind(e,21933))},45822:function(n,u,e){"use strict";e.d(u,{JO:function(){return s.Z},JX:function(){return t.Z},rj:function(){return c.Z},xv:function(){return i.Z},zx:function(){return r.Z}});var r=e(20831),t=e(49804),c=e(67101),s=e(47323),i=e(84264)},21933:function(n,u,e){"use strict";e.r(u);var r=e(57437),t=e(42273),c=e(39760);u.default=()=>{let{accessToken:n,userId:u,userRole:e}=(0,c.Z)();return(0,r.jsx)(t.Z,{accessToken:n,userID:u,userRole:e})}}},function(n){n.O(0,[9820,1491,1526,2417,3709,9775,2525,1529,2284,7908,9011,9678,3603,5319,1853,6494,5188,4924,8049,131,2202,874,2273,2971,2117,1744],function(){return n(n.s=86947)}),_N_E=n.O()}]);
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||||
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|
|
@ -1,14 +1,14 @@
|
|||
2:I[19107,[],"ClientPageRoot"]
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||||
3:I[81300,["9820","static/chunks/9820-b0722f821c1af1ee.js","2926","static/chunks/2926-b5dcad9f62f5e2b1.js","7906","static/chunks/7906-11071e9e2e7b8318.js","4303","static/chunks/app/(dashboard)/api-reference/page-e1b30f2f59900b67.js"],"default",1]
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||||
3:I[81300,["9820","static/chunks/9820-b0722f821c1af1ee.js","2926","static/chunks/2926-b5dcad9f62f5e2b1.js","7906","static/chunks/7906-11071e9e2e7b8318.js","4303","static/chunks/app/(dashboard)/api-reference/page-c04f26edd6161f83.js"],"default",1]
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||||
4:I[4707,[],""]
|
||||
5:I[36423,[],""]
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||||
6:I[89219,["9820","static/chunks/9820-b0722f821c1af1ee.js","1491","static/chunks/1491-8280340b5391aa11.js","1526","static/chunks/1526-8e2976ed10cb2fa0.js","3709","static/chunks/3709-b17db86e0ab325d2.js","1529","static/chunks/1529-e0933e3af843b646.js","3603","static/chunks/3603-b101c17ea3d68f19.js","9165","static/chunks/9165-2a738a73d0d5f1d1.js","8098","static/chunks/8098-3da3212991542668.js","8049","static/chunks/8049-26a565b2b753c5e8.js","2019","static/chunks/2019-f91b853dc598350e.js","5642","static/chunks/app/(dashboard)/layout-0374fe6c07ff896f.js"],"default",1]
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||||
6:I[89219,["9820","static/chunks/9820-b0722f821c1af1ee.js","1491","static/chunks/1491-8280340b5391aa11.js","1526","static/chunks/1526-8e2976ed10cb2fa0.js","3709","static/chunks/3709-b17db86e0ab325d2.js","1529","static/chunks/1529-e0933e3af843b646.js","3603","static/chunks/3603-b101c17ea3d68f19.js","9165","static/chunks/9165-2a738a73d0d5f1d1.js","8098","static/chunks/8098-3da3212991542668.js","8049","static/chunks/8049-f268e4c9d6244ca6.js","2019","static/chunks/2019-f91b853dc598350e.js","5642","static/chunks/app/(dashboard)/layout-0374fe6c07ff896f.js"],"default",1]
|
||||
8:I[31857,["3185","static/chunks/app/layout-b4b61d636c5d2baf.js"],"FeatureFlagsProvider"]
|
||||
7:{}
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||||
9:{"fontFamily":"system-ui,\"Segoe UI\",Roboto,Helvetica,Arial,sans-serif,\"Apple Color Emoji\",\"Segoe UI Emoji\"","height":"100vh","textAlign":"center","display":"flex","flexDirection":"column","alignItems":"center","justifyContent":"center"}
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||||
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||||
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||||
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