Updated readme for Qdrant

README.md

@@ -9,20 +9,13 @@
 
 ## Introduction
 
-TrustGraph is a true end-to-end (e2e) knowledge pipeline that performs a `Naive Extraction` on a text corpus
-to build a RDF style knowledge graph coupled with a `RAG` service compatible with cloud LLMs and open-source
-SLMs (Small Language Models).
+TrustGraph is a true end-to-end (e2e) knowledge pipeline that performs a `Naive Extraction` on a text corpus to build a RDF style knowledge graph coupled with a `RAG` service compatible with cloud LLMs and open-source SLMs (Small Language Models).
 
-The pipeline processing components are interconnected with a pub/sub engine to
-maximize modularity and enable new knowledge processing functions. The core processing components decode documents, 
-chunk text, perform embeddings, apply a local SLM/LLM, call a LLM API, and generate LM predictions.
+The pipeline processing components are interconnected with a pub/sub engine to maximize modularity and enable new knowledge processing functions. The core processing components decode documents, chunk text, perform embeddings, apply a local SLM/LLM, call a LLM API, and generate LM predictions.
 
-The processing showcases the reliability and efficiences of Graph RAG algorithms which can capture
-contextual language flags that are missed in conventional RAG approaches. Graph querying algorithms enable retrieving
-not just relevant knowledge but language cues essential to understanding semantic uses unique to a text corpus.
+The processing showcases the reliability and efficiences of Graph RAG algorithms which can capture contextual language flags that are missed in conventional RAG approaches. Graph querying algorithms enable retrieving not just relevant knowledge but language cues essential to understanding semantic uses unique to a text corpus.
 
-Processing modules are executed in containers.  Processing can be scaled-up
-by deploying multiple containers.
+Processing modules are executed in containers.  Processing can be scaled-up by deploying multiple containers.
 
 ### Features
 
@@ -34,67 +27,42 @@ by deploying multiple containers.
 - Application of a [HuggingFace](https://hf.co) embeddings models
 - [RDF](https://www.w3.org/TR/rdf12-schema/)-aligned Knowledge Graph extraction
 - Graph edge loading into [Apache Cassandra](https://github.com/apache/cassandra) or [Neo4j](https://neo4j.com/)
-- Storing embeddings in [Milvus](https://github.com/milvus-io/milvus)
+- Storing embeddings in [Qdrant](https://qdrant.tech/)
 - Build and load [Knowledge Cores](https://trustgraph.ai/docs/category/knowledge-cores)
 - Embedding query service
 - Graph RAG query service
 - All procesing integrates with [Apache Pulsar](https://github.com/apache/pulsar/)
-- Containers, can be deployed using `Docker` or [Podman](http://podman.io/)
+- Containers deployed using `Docker` or [Podman](http://podman.io/)
 - Plug'n'play architecture: switch different LLM modules to suit your needs
 
 ## Architecture
 
 ![architecture](architecture.png)
 
-TrustGraph is designed to be modular to support as many Language Models and environments as possible. A natural
-fit for a modular architecture is to decompose functions into a set modules connected through a pub/sub backbone.
-[Apache Pulsar](https://github.com/apache/pulsar/) serves as this pub/sub backbone. Pulsar acts as the data broker
-managing inputs and outputs between modules.
+TrustGraph is designed to be modular to support as many Language Models and environments as possible. A natural fit for a modular architecture is to decompose functions into a set modules connected through a pub/sub backbone. [Apache Pulsar](https://github.com/apache/pulsar/) serves as this pub/sub backbone. Pulsar acts as the data broker managing inputs and outputs between modules.
 
 **Pulsar Workflows**:
-- For processing flows, Pulsar accepts the output of a processing module
-  and queues it for input to the next subscribed module.
-- For services such as LLMs and embeddings, Pulsar provides a client/server
-  model.  A Pulsar queue is used as the input to the service.  When
-  processed, the output is then delivered to a separate queue where a client
-  subscriber can request that output.
 
-The entire architecture, the pub/sub backbone and set of modules, is bundled into a single Python package. A container image with the
-package installed can also run the entire architecture.
+- For processing flows, Pulsar accepts the output of a processing module and queues it for input to the next subscribed module.
+- For services such as LLMs and embeddings, Pulsar provides a client/server model.  A Pulsar queue is used as the input to the service.  When processed, the output is then delivered to a separate queue where a client subscriber can request that output.
+
+The entire architecture, the pub/sub backbone and set of modules, is bundled into a single Python package. A container image with the package installed can also run the entire architecture.
 
 ## Core Modules
 
-- `chunker-recursive` - Accepts text documents and uses LangChain recursive
-  chunking algorithm to produce smaller text chunks.
+- `chunker-recursive` - Accepts text documents and uses LangChain recursive chunking algorithm to produce smaller text chunks.
 - `chunker-token` - Chunks texts documents by a chosen amount of tokens.
-- `embeddings-hf` - A service which analyses text and returns a vector
-  embedding using one of the HuggingFace embeddings models.
-- `embeddings-ollama` - A service which analyses text and returns a vector
-  embedding using an Ollama embeddings model.
-- `embeddings-vectorize` - Uses an embeddings service to get a vector
-  embedding which is added to the processor payload.
-- `graph-rag` - A query service which applies a Graph RAG algorithm to
-  provide a response to a text prompt.
-- `triples-write-cassandra` - Takes knowledge graph edges and writes them to
-  a Cassandra store.
-- `triples-write-neo4j` - Takes knowledge graph edges and writes them to
-  a Neo4j store.
-- `kg-extract-definitions` - knowledge extractor - examines text and
-  produces graph edges.
-  describing discovered terms and also their defintions.  Definitions are
-  derived using the input  documents.
-- `kg-extract-relationships` - knowledge extractor - examines text and
-  produces graph edges describing the relationships between discovered
-  terms.
-- `loader` - Takes a document and loads into the processing pipeline.  Used
-  e.g. to add PDF documents.
-- `pdf-decoder` - Takes a PDF doc and emits text extracted from the document.
-  Text extraction from PDF is not a perfect science as PDF is a printable
-  format.  For instance, the wrapping of text between lines in a PDF document
-  is not semantically encoded, so the decoder will see wrapped lines as
-  space-separated.
-- `ge-write-milvus` - Takes graph embeddings mappings and records them
-  in the vector embeddings store.
+- `embeddings-hf` - A service which analyses text and returns a vector embedding using one of the HuggingFace embeddings models.
+- `embeddings-ollama` - A service which analyses text and returns a vector embedding using an Ollama embeddings model.
+- `embeddings-vectorize` - Uses an embeddings service to get a vector embedding which is added to the processor payload.
+- `graph-rag` - A query service which applies a Graph RAG algorithm to provide a response to a text prompt.
+- `triples-write-cassandra` - Takes knowledge graph edges and writes them to a Cassandra store.
+- `triples-write-neo4j` - Takes knowledge graph edges and writes them to a Neo4j store.
+- `kg-extract-definitions` - knowledge extractor - examines text and produces graph edges. describing discovered terms and also their defintions.  Definitions are derived using the input  documents.
+- `kg-extract-relationships` - knowledge extractor - examines text and produces graph edges describing the relationships between discovered terms.
+- `loader` - Takes a document and loads into the processing pipeline.  Used e.g. to add PDF documents.
+- `pdf-decoder` - Takes a PDF doc and emits text extracted from the document. Text extraction from PDF is not a perfect science as PDF is a printable format.  For instance, the wrapping of text between lines in a PDF document is not semantically encoded, so the decoder will see wrapped lines as space-separated.
+- `ge-write-qdrant` - Takes graph embeddings mappings and records them in the vector embeddings store.
 
 ## LM Specific Modules