TxtAI MCP Server

# Similarity   The Similarity pipeline computes similarity between queries and list of text using a text classifier. This pipeline supports both standard text classification models and zero-shot classification models. The pipeline uses the queries as labels for the input text. The results are transposed to get scores per query/label vs scores per input text. Cross-encoder models are supported via the `crossencode=True` constructor parameter. Late interaction (i.e. ColBERT) models are also supported via the `lateencode=True` constructor parameter. CrossEncoder and LateEncoder pipelines back each of these models and can be instantiated directly as well. ## Example The following shows a simple example using this pipeline. ```python from txtai.pipeline import Similarity # Create and run pipeline similarity = Similarity() similarity("feel good story", [ "Maine man wins $1M from $25 lottery ticket", "Don't sacrifice slower friends in a bear attack" ]) ``` See the link below for a more detailed example. | Notebook | Description | | |:----------|:-------------|------:| | [Add semantic search to Elasticsearch](https://github.com/neuml/txtai/blob/master/examples/04_Add_semantic_search_to_Elasticsearch.ipynb) | Add semantic search to existing search systems | [](https://colab.research.google.com/github/neuml/txtai/blob/master/examples/04_Add_semantic_search_to_Elasticsearch.ipynb) | ## Configuration-driven example Pipelines are run with Python or configuration. Pipelines can be instantiated in [configuration](../../../api/configuration/#pipeline) using the lower case name of the pipeline. Configuration-driven pipelines are run with [workflows](../../../workflow/#configuration-driven-example) or the [API](../../../api#local-instance). ### config.yml ```yaml # Create pipeline using lower case class name similarity: ``` ### Run with Workflows ```python from txtai import Application # Create and run pipeline with workflow app = Application("config.yml") app.similarity("feel good story", [ "Maine man wins $1M from $25 lottery ticket", "Don't sacrifice slower friends in a bear attack" ]) ``` ### Run with API ```bash CONFIG=config.yml uvicorn "txtai.api:app" & curl \ -X POST "http://localhost:8000/similarity" \ -H "Content-Type: application/json" \ -d '{"query": "feel good story", "texts": ["Maine man wins $1M from $25 lottery ticket", "Dont sacrifice slower friends in a bear attack"]}' ``` ## Methods Python documentation for the pipeline. ### ::: txtai.pipeline.Similarity.__init__ ### ::: txtai.pipeline.Similarity.__call__