Add parameter for setting the textractor backend, closes #22

README.md

@@ -115,6 +115,7 @@ The RAG application has a number of environment variables that can be set to con
 | EMBEDDINGS  | Embeddings database path                    | [neuml/txtai-wikipedia-slim](https://hf.co/NeuML/txtai-wikipedia-slim) |
 | MAXLENGTH   | Maximum generation length                   | 2048 for topics, 4096 for RAG       |
 | CONTEXT     | RAG context size                            | 10                                  |
+| TEXTBACKEND | [Text extraction backend](https://neuml.github.io/txtai/pipeline/data/filetohtml/#txtai.pipeline.FileToHTML.__init__)                     | available                         |
 | DATA        | Optional directory to index data from       | None                                |
 | PERSIST     | Optional directory to save index updates to | None                                |
 | TOPICSBATCH | Optional batch size for LLM topic queries   | None                                |
@@ -151,12 +152,24 @@ docker run -d --gpus=all -it -p 8501:8501 -e LLM=gpt-4o -e OPENAI_API_KEY=your-a
 docker run -d --gpus=all -it -p 8501:8501 -e EMBEDDINGS=neuml/arxiv neuml/rag
 ```
 
+### Start with an empty embeddings index
+
+```
+docker run -d --gpus=all -it -p 8501:8501 -e EMBEDDINGS= neuml/rag
+```
+
 ### Build an embeddings index with a local directory of files
 
 ```
 docker run -d --gpus=all -it -p 8501:8501 -e DATA=/data/path -v local/path:/data/path neuml/rag
 ```
 
+### Use the Docling text extraction backend
+
+```
+docker run -d --gpus=all -it -p 8501:8501 -e TEXTBACKEND=docling neuml/rag
+```
+
 ### Persist embeddings and cache models
 
 ```

rag.py

@@ -319,6 +319,9 @@ def __init__(self):
             context=self.context,
         )
 
+        # Textractor instance (lazy loaded)
+        self.textractor = None
+
     def load(self):
         """
         Creates or loads an Embeddings instance.
@@ -425,8 +428,15 @@ def extract(self, inputs):
             extracted content
         """
 
-        textractor = Textractor(paragraphs=True)
-        return textractor(inputs)
+        # Initialize textractor
+        if not self.textractor:
+            self.textractor = Textractor(
+                paragraphs=True,
+                backend=os.environ.get("TEXTBACKEND", "available"),
+            )
+
+        # Extract text
+        return self.textractor(inputs)
 
     def infertopics(self, embeddings, start):
         """