Merge branch 'main' into feature/ollama_streaming_support

integrations/chroma/src/haystack_integrations/components/retrievers/chroma/retriever.py

@@ -66,7 +66,7 @@ class ChromaEmbeddingRetriever(ChromaQueryRetriever):
     def run(
         self,
         query_embedding: List[float],
-        _: Optional[Dict[str, Any]] = None,  # filters not yet supported
+        filters: Optional[Dict[str, Any]] = None,
         top_k: Optional[int] = None,
     ):
         """
@@ -80,4 +80,4 @@ def run(
         top_k = top_k or self.top_k
 
         query_embeddings = [query_embedding]
-        return {"documents": self.document_store.search_embeddings(query_embeddings, top_k)[0]}
+        return {"documents": self.document_store.search_embeddings(query_embeddings, top_k, filters)[0]}

integrations/chroma/src/haystack_integrations/document_stores/chroma/document_store.py

@@ -24,7 +24,11 @@ class ChromaDocumentStore:
     """
 
     def __init__(
-        self, collection_name: str = "documents", embedding_function: str = "default", **embedding_function_params
+        self,
+        collection_name: str = "documents",
+        embedding_function: str = "default",
+        persist_path: Optional[str] = None,
+        **embedding_function_params,
     ):
         """
         Initializes the store. The __init__ constructor is not part of the Store Protocol
@@ -40,7 +44,10 @@ def __init__(
         self._embedding_function = embedding_function
         self._embedding_function_params = embedding_function_params
         # Create the client instance
-        self._chroma_client = chromadb.Client()
+        if persist_path is None:
+            self._chroma_client = chromadb.Client()
+        else:
+            self._chroma_client = chromadb.PersistentClient(path=persist_path)
         self._collection = self._chroma_client.get_or_create_collection(
             name=collection_name,
             embedding_function=get_embedding_function(embedding_function, **embedding_function_params),
@@ -185,16 +192,31 @@ def search(self, queries: List[str], top_k: int) -> List[List[Document]]:
         )
         return self._query_result_to_documents(results)
 
-    def search_embeddings(self, query_embeddings: List[List[float]], top_k: int) -> List[List[Document]]:
+    def search_embeddings(
+        self, query_embeddings: List[List[float]], top_k: int, filters: Optional[Dict[str, Any]] = None
+    ) -> List[List[Document]]:
         """
         Perform vector search on the stored document, pass the embeddings of the queries
-        instead of their text
+        instead of their text.
+
+        Accepts filters in haystack format.
         """
-        results = self._collection.query(
-            query_embeddings=query_embeddings,
-            n_results=top_k,
-            include=["embeddings", "documents", "metadatas", "distances"],
-        )
+        if filters is None:
+            results = self._collection.query(
+                query_embeddings=query_embeddings,
+                n_results=top_k,
+                include=["embeddings", "documents", "metadatas", "distances"],
+            )
+        else:
+            chroma_filters = self._normalize_filters(filters=filters)
+            results = self._collection.query(
+                query_embeddings=query_embeddings,
+                n_results=top_k,
+                where=chroma_filters[1],
+                where_document=chroma_filters[2],
+                include=["embeddings", "documents", "metadatas", "distances"],
+            )
+
         return self._query_result_to_documents(results)
 
     @classmethod

integrations/uptrain/src/haystack_integrations/components/evaluators/evaluator.py

@@ -19,8 +19,8 @@ class UpTrainEvaluator:
     A component that uses the UpTrain framework to evaluate inputs against a specific metric.
 
     The supported metrics are defined by :class:`UpTrainMetric`. The inputs of the component
-    metric-dependent. The output is a list of :class:`UpTrainEvaluatorOutput` objects, each
-    containing a single input and the result of the evaluation performed on it.
+    metric-dependent. The output is a nested list of evaluation results where each inner list
+    contains the results for a single input.
     """
 
     _backend_metric: Union[Evals, ParametricEval]