k

backend/danswer/configs/chat_configs.py

@@ -61,7 +61,7 @@ HYBRID_ALPHA = max(0, min(1, float(os.environ.get("HYBRID_ALPHA") or 0.62)))
 # Content. This is to avoid cases where the Content is very relevant but it may not be clear
 # if the title is separated out. Title is most of a "boost" than a separate field.
 TITLE_CONTENT_RATIO = max(
-    0, min(1, float(os.environ.get("TITLE_CONTENT_RATIO") or 0.20))
+    0, min(1, float(os.environ.get("TITLE_CONTENT_RATIO") or 0.10))
 )
 # A list of languages passed to the LLM to rephase the query
 # For example "English,French,Spanish", be sure to use the "," separator

backend/danswer/document_index/vespa/app_config/schemas/danswer_chunk.sd

@@ -20,18 +20,10 @@ schema DANSWER_CHUNK_NAME {
         # `semantic_identifier` will be the channel name, but the `title` will be empty
         field title type string {
             indexing: summary | index | attribute
-            match {
-                gram
-                gram-size: 3
-            }
             index: enable-bm25
         }
         field content type string {
             indexing: summary | index
-            match {
-                gram
-                gram-size: 3
-            }
             index: enable-bm25
         }
         # duplication of `content` is far from ideal, but is needed for 
@@ -153,43 +145,40 @@ schema DANSWER_CHUNK_NAME {
             query(query_embedding) tensor<float>(x[VARIABLE_DIM])
         }
 
-        # This must be separate function for normalize_linear to work
-        function vector_score() {
+        function title_vector_score() {
             expression {
                 # If no title, the full vector score comes from the content embedding
-                (query(title_content_ratio) * if(attribute(skip_title), closeness(field, embeddings), closeness(field, title_embedding))) +
-                ((1 - query(title_content_ratio)) * closeness(field, embeddings))
-            }
-        }
-
-        # This must be separate function for normalize_linear to work
-        function keyword_score() {
-            expression {
-                (query(title_content_ratio) * bm25(title)) +
-                ((1 - query(title_content_ratio)) * bm25(content))
+                if(attribute(skip_title), closeness(field, embeddings), closeness(field, title_embedding))
             }
         }
 
         first-phase {
-            expression: vector_score
+            expression: closeness(field, embeddings)
         }
 
         # Weighted average between Vector Search and BM-25
-        # Each is a weighted average between the Title and Content fields
-        # Finally each doc is boosted by it's user feedback based boost and recency
-        # If any embedding or index field is missing, it just receives a score of 0
-        # Assumptions:
-        # - For a given query + corpus, the BM-25 scores will be relatively similar in distribution
-        #   therefore not normalizing before combining.
-        # - For documents without title, it gets a score of 0 for that and this is ok as documents
-        #   without any title match should be penalized.
         global-phase {
             expression {
                 (
                     # Weighted Vector Similarity Score
-                    (query(alpha) * normalize_linear(vector_score)) +
+                    (
+                        query(alpha) * (
+                            (query(title_content_ratio) * normalize_linear(title_vector_score))
+                            +
+                            ((1 - query(title_content_ratio)) * normalize_linear(closeness(field, embeddings)))
+                        )
+                    )
+
+                    +
+
                     # Weighted Keyword Similarity Score
-                    ((1 - query(alpha)) * normalize_linear(keyword_score))
+                    (
+                        (1 - query(alpha)) * (
+                            (query(title_content_ratio) * normalize_linear(bm25(title)))
+                            +
+                            ((1 - query(title_content_ratio)) * normalize_linear(bm25(content)))
+                        )
+                    )
                 )
                 # Boost based on user feedback
                 * document_boost
@@ -204,8 +193,6 @@ schema DANSWER_CHUNK_NAME {
             bm25(content)
             closeness(field, title_embedding)
             closeness(field, embeddings)
-            keyword_score
-            vector_score
             document_boost
             recency_bias
             closest(embeddings)