Testing retriever and updating docs with logging

docetl/operations/base.py

@@ -2,6 +2,7 @@
 The BaseOperation class is an abstract base class for all operations in the docetl framework. It provides a common structure and interface for various data processing operations.
 """
 
+import traceback
 from abc import ABC, ABCMeta, abstractmethod
 from typing import Any
 
@@ -87,6 +88,7 @@ class BaseOperation(ABC, metaclass=BaseOperationMeta):
         type: str
         skip_on_error: bool = False
         gleaning: GleaningConfig | None = None
+        retriever: str | None = None
 
     @abstractmethod
     def execute(self, input_data: list[dict]) -> tuple[list[dict], float]:
@@ -109,3 +111,26 @@ class BaseOperation(ABC, metaclass=BaseOperationMeta):
         """Perform syntax checks on the operation configuration."""
         # Validate the configuration using Pydantic
         self.schema.model_validate(self.config, context=context)
+
+    def _maybe_build_retrieval_context(self, context: dict[str, Any]) -> str:
+        """Build retrieval context string if a retriever is configured."""
+        retriever_name = self.config.get("retriever")
+        if not retriever_name:
+            return ""
+        retrievers = getattr(self.runner, "retrievers", {})
+        if retriever_name not in retrievers:
+            raise ValueError(
+                f"Retriever '{retriever_name}' not found in configuration."
+            )
+        retriever = retrievers[retriever_name]
+        try:
+            result = retriever.retrieve(context)
+            return result.rendered_context or ""
+        except Exception as e:
+            # Soft-fail to avoid blocking the op
+            self.console.log(
+                f"[yellow]Warning: retrieval failed for '{retriever_name}': {e}[/yellow]"
+            )
+            # Print traceback to help debug
+            self.console.log(traceback.format_exc())
+            return "No extra context available."

docetl/operations/extract.py

@@ -118,7 +118,7 @@ class ExtractOperation(BaseOperation):
 
     def _execute_line_number_strategy(
         self, item: dict, doc_key: str
-    ) -> tuple[list[dict[str, Any]], float]:
+    ) -> tuple[list[str], float, str]:
         """
         Executes the line number extraction strategy for a single document key.
 
@@ -147,10 +147,18 @@ class ExtractOperation(BaseOperation):
         formatted_text = self._reformat_text_with_line_numbers(text_content)
 
         # Render the prompt
-        extraction_instructions = strict_render(self.config["prompt"], {"input": item})
+        # Retrieval context
+        retrieval_context = self._maybe_build_retrieval_context({"input": item})
+        extraction_instructions = strict_render(
+            self.config["prompt"],
+            {"input": item, "retrieval_context": retrieval_context},
+        )
         augmented_prompt_template = """
 You are extracting specific content from text documents. Extract information according to these instructions: {{ extraction_instructions }}
 
+Extra context (may be helpful):
+{{ retrieval_context }}
+
 The text is formatted with line numbers as follows:
 {{ formatted_text }}
 
@@ -177,6 +185,7 @@ Do not include explanatory text in your response, only the JSON object.
             {
                 "extraction_instructions": extraction_instructions,
                 "formatted_text": formatted_text,
+                "retrieval_context": retrieval_context,
             },
         )
 
@@ -217,7 +226,7 @@ Do not include explanatory text in your response, only the JSON object.
                 self.console.log(
                     f"[bold red]Error parsing LLM response: {llm_result.response}. Skipping.[/bold red]"
                 )
-                return [], llm_result.total_cost
+                return [], llm_result.total_cost, retrieval_context
 
             for line_range in parsed_output.get("line_ranges", []):
                 start_line = line_range.get("start_line", 0)
@@ -245,20 +254,20 @@ Do not include explanatory text in your response, only the JSON object.
 
                 extracted_texts.append("".join(extracted_content))
 
-            return extracted_texts, llm_result.total_cost
+            return extracted_texts, llm_result.total_cost, retrieval_context
 
         except Exception as e:
             if self.config.get("skip_on_error", True):
                 self.console.log(
                     f"[bold red]Error parsing LLM response: {str(e)}. Skipping.[/bold red]"
                 )
-                return [], llm_result.total_cost
+                return [], llm_result.total_cost, retrieval_context
             else:
                 raise RuntimeError(f"Error parsing LLM response: {str(e)}") from e
 
     def _execute_regex_strategy(
         self, item: dict, doc_key: str
-    ) -> tuple[list[str], float]:
+    ) -> tuple[list[str], float, str]:
         """
         Executes the regex extraction strategy for a single document key.
 
@@ -267,7 +276,7 @@ Do not include explanatory text in your response, only the JSON object.
             doc_key (str): The key of the document text to process.
 
         Returns:
-            tuple[list[str], float]: A tuple containing the extraction results and the cost.
+            tuple[list[str], float, str]: A tuple containing the extraction results, cost, and retrieval context.
         """
         import re
 
@@ -277,7 +286,7 @@ Do not include explanatory text in your response, only the JSON object.
                 self.console.log(
                     f"[yellow]Warning: Key '{doc_key}' not found or not a string in document. Skipping.[/yellow]"
                 )
-                return [], 0.0
+                return [], 0.0, ""
             else:
                 raise ValueError(
                     f"Key '{doc_key}' not found or not a string in document"
@@ -286,13 +295,21 @@ Do not include explanatory text in your response, only the JSON object.
         text_content = item[doc_key]
 
         # Prepare the context for prompt rendering
-        context = {"input": item, "text_content": text_content}
+        retrieval_context = self._maybe_build_retrieval_context({"input": item})
+        context = {
+            "input": item,
+            "text_content": text_content,
+            "retrieval_context": retrieval_context,
+        }
 
         # Render the prompt
         extraction_instructions = strict_render(self.config["prompt"], context)
         augmented_prompt_template = """
 You are creating regex patterns to extract specific content from text. Extract information according to these instructions: {{ extraction_instructions }}
 
+Extra context (may be helpful):
+{{ retrieval_context }}
+
 The text to analyze is:
 {{ text_content }}
 
@@ -371,14 +388,14 @@ Return only the JSON object with your patterns, no explanatory text.
                     else:
                         raise ValueError(f"Invalid regex pattern '{pattern}': {str(e)}")
 
-            return extracted_texts, llm_result.total_cost
+            return extracted_texts, llm_result.total_cost, retrieval_context
 
         except Exception as e:
             if self.config.get("skip_on_error", True):
                 self.console.log(
                     f"[bold red]Error parsing LLM response: {str(e)}. Skipping.[/bold red]"
                 )
-                return [], llm_result.total_cost
+                return [], llm_result.total_cost, retrieval_context
             else:
                 raise RuntimeError(f"Error parsing LLM response: {str(e)}") from e
 
@@ -446,7 +463,7 @@ Return only the JSON object with your patterns, no explanatory text.
                 doc_key, future, output_item = futures[i]
 
                 try:
-                    extracted_texts_duped, cost = future.result()
+                    extracted_texts_duped, cost, retrieval_context = future.result()
 
                     # Remove duplicates and empty strings
                     extracted_texts_duped = [
@@ -468,6 +485,12 @@ Return only the JSON object with your patterns, no explanatory text.
                     else:
                         output_item[output_key] = extracted_texts
 
+                    # Save retrieved context if enabled
+                    if self.config.get("save_retriever_output", False):
+                        output_item[f"_{self.config['name']}_retrieved_context"] = (
+                            retrieval_context if retrieval_context else ""
+                        )
+
                 except Exception as e:
                     if self.config.get("skip_on_error", True):
                         self.console.log(

docetl/operations/filter.py

@@ -87,6 +87,7 @@ class FilterOperation(MapOperation):
             )
         )
 
+        # Inject retrieval into inner map execution (super)
         results, total_cost = super().execute(input_data)
 
         # Drop records with filter_key values that are False

docetl/operations/map.py

@@ -9,8 +9,6 @@ from typing import Any
 
 import requests
 from jinja2 import Template
-
-from docetl.utils import has_jinja_syntax, prompt_user_for_non_jinja_confirmation
 from litellm.utils import ModelResponse
 from pydantic import Field, field_validator, model_validator
 from tqdm import tqdm
@@ -19,6 +17,7 @@ from docetl.base_schemas import Tool, ToolFunction
 from docetl.operations.base import BaseOperation
 from docetl.operations.utils import RichLoopBar, strict_render, validate_output_types
 from docetl.operations.utils.api import OutputMode
+from docetl.utils import has_jinja_syntax, prompt_user_for_non_jinja_confirmation
 
 
 class MapOperation(BaseOperation):
@@ -309,7 +308,17 @@ Reference anchors:"""
             item: dict, initial_result: dict | None = None
         ) -> tuple[dict | None, float]:
 
-            prompt = strict_render(self.config["prompt"], {"input": item})
+            # Build retrieval context (if configured)
+            retrieval_context = self._maybe_build_retrieval_context({"input": item})
+            ctx = {"input": item, "retrieval_context": retrieval_context}
+            rendered = strict_render(self.config["prompt"], ctx)
+            # If template didn't use retrieval_context, prepend a standard header
+            prompt = (
+                f"Here is some extra context:\n{retrieval_context}\n\n{rendered}"
+                if retrieval_context
+                and "retrieval_context" not in self.config["prompt"]
+                else rendered
+            )
             messages = [{"role": "user", "content": prompt}]
             if self.config.get("pdf_url_key", None):
                 # Append the pdf to the prompt
@@ -417,6 +426,12 @@ Reference anchors:"""
                         output[f"_observability_{self.config['name']}"] = {
                             "prompt": prompt
                         }
+                # Add retrieved context if save_retriever_output is enabled
+                if self.config.get("save_retriever_output", False):
+                    for output in outputs:
+                        output[f"_{self.config['name']}_retrieved_context"] = (
+                            retrieval_context if retrieval_context else ""
+                        )
                 return outputs, llm_result.total_cost
 
             return None, llm_result.total_cost

docetl/operations/reduce.py

@@ -297,6 +297,16 @@ class ReduceOperation(BaseOperation):
                 group_list = group_elems
 
             total_cost = 0.0
+            # Build retrieval context once per group
+            try:
+                retrieval_context = self._maybe_build_retrieval_context(
+                    {
+                        "reduce_key": dict(zip(self.config["reduce_key"], key)),
+                        "inputs": group_list,
+                    }
+                )
+            except Exception:
+                retrieval_context = "No extra context available."
 
             # Apply value sampling if enabled
             value_sampling = self.config.get("value_sampling", {})
@@ -326,18 +336,26 @@ class ReduceOperation(BaseOperation):
 
             # Only execute merge-based plans if associative = True
             if "merge_prompt" in self.config and self.config.get("associative", True):
-                result, prompts, cost = self._parallel_fold_and_merge(key, group_list)
+                result, prompts, cost = self._parallel_fold_and_merge(
+                    key, group_list, retrieval_context
+                )
             elif self.config.get("fold_batch_size", None) and self.config.get(
                 "fold_batch_size"
             ) >= len(group_list):
                 # If the fold batch size is greater than or equal to the number of items in the group,
                 # we can just run a single fold operation
-                result, prompt, cost = self._batch_reduce(key, group_list)
+                result, prompt, cost = self._batch_reduce(
+                    key, group_list, None, retrieval_context
+                )
                 prompts = [prompt]
             elif "fold_prompt" in self.config:
-                result, prompts, cost = self._incremental_reduce(key, group_list)
+                result, prompts, cost = self._incremental_reduce(
+                    key, group_list, retrieval_context
+                )
             else:
-                result, prompt, cost = self._batch_reduce(key, group_list)
+                result, prompt, cost = self._batch_reduce(
+                    key, group_list, None, retrieval_context
+                )
                 prompts = [prompt]
 
             total_cost += cost
@@ -349,6 +367,16 @@ class ReduceOperation(BaseOperation):
                 # Add the _observability_{self.config['name']} key to the result
                 result[f"_observability_{self.config['name']}"] = {"prompts": prompts}
 
+            # Add retrieved context if save_retriever_output is enabled
+            if self.config.get("save_retriever_output", False):
+                ctx = (
+                    retrieval_context
+                    if retrieval_context
+                    and retrieval_context != "No extra context available."
+                    else ""
+                )
+                result[f"_{self.config['name']}_retrieved_context"] = ctx
+
             # Apply pass-through at the group level
             if (
                 result is not None
@@ -467,7 +495,7 @@ class ReduceOperation(BaseOperation):
         return [group_list[i] for i in top_k_indices], cost
 
     def _parallel_fold_and_merge(
-        self, key: tuple, group_list: list[dict]
+        self, key: tuple, group_list: list[dict], retrieval_context: str
     ) -> tuple[dict | None, float]:
         """
         Perform parallel folding and merging on a group of items.
@@ -632,7 +660,7 @@ class ReduceOperation(BaseOperation):
         )
 
     def _incremental_reduce(
-        self, key: tuple, group_list: list[dict]
+        self, key: tuple, group_list: list[dict], retrieval_context: str
     ) -> tuple[dict | None, list[str], float]:
         """
         Perform an incremental reduce operation on a group of items.
@@ -728,6 +756,7 @@ class ReduceOperation(BaseOperation):
         batch: list[dict],
         current_output: dict | None,
         scratchpad: str | None = None,
+        retrieval_context: str | None = None,
     ) -> tuple[dict | None, str, float]:
         """
         Perform an incremental fold operation on a batch of items.
@@ -744,7 +773,7 @@ class ReduceOperation(BaseOperation):
             the prompt used, and the cost of the fold operation.
         """
         if current_output is None:
-            return self._batch_reduce(key, batch, scratchpad)
+            return self._batch_reduce(key, batch, scratchpad, retrieval_context)
 
         start_time = time.time()
         fold_prompt = strict_render(
@@ -753,8 +782,15 @@ class ReduceOperation(BaseOperation):
                 "inputs": batch,
                 "output": current_output,
                 "reduce_key": dict(zip(self.config["reduce_key"], key)),
+                "retrieval_context": retrieval_context or "",
             },
         )
+        if retrieval_context and "retrieval_context" not in self.config.get(
+            "fold_prompt", ""
+        ):
+            fold_prompt = (
+                f"Here is some extra context:\n{retrieval_context}\n\n{fold_prompt}"
+            )
 
         response = self.runner.api.call_llm(
             self.config.get("model", self.default_model),
@@ -801,7 +837,7 @@ class ReduceOperation(BaseOperation):
         return None, fold_prompt, fold_cost
 
     def _merge_results(
-        self, key: tuple, outputs: list[dict]
+        self, key: tuple, outputs: list[dict], retrieval_context: str | None = None
     ) -> tuple[dict | None, str, float]:
         """
         Merge multiple outputs into a single result.
@@ -822,8 +858,15 @@ class ReduceOperation(BaseOperation):
             {
                 "outputs": outputs,
                 "reduce_key": dict(zip(self.config["reduce_key"], key)),
+                "retrieval_context": retrieval_context or "",
             },
         )
+        if retrieval_context and "retrieval_context" not in self.config.get(
+            "merge_prompt", ""
+        ):
+            merge_prompt = (
+                f"Here is some extra context:\n{retrieval_context}\n\n{merge_prompt}"
+            )
         response = self.runner.api.call_llm(
             self.config.get("model", self.default_model),
             "merge",
@@ -918,7 +961,11 @@ class ReduceOperation(BaseOperation):
             self.merge_times.append(time)
 
     def _batch_reduce(
-        self, key: tuple, group_list: list[dict], scratchpad: str | None = None
+        self,
+        key: tuple,
+        group_list: list[dict],
+        scratchpad: str | None = None,
+        retrieval_context: str | None = None,
     ) -> tuple[dict | None, str, float]:
         """
         Perform a batch reduce operation on a group of items.
@@ -938,8 +985,13 @@ class ReduceOperation(BaseOperation):
             {
                 "reduce_key": dict(zip(self.config["reduce_key"], key)),
                 "inputs": group_list,
+                "retrieval_context": retrieval_context or "",
             },
         )
+        if retrieval_context and "retrieval_context" not in self.config.get(
+            "prompt", ""
+        ):
+            prompt = f"Here is some extra context:\n{retrieval_context}\n\n{prompt}"
         item_cost = 0
 
         response = self.runner.api.call_llm(

docetl/retrievers/__init__.py

@@ -0,0 +1,6 @@
+from .base import Retriever
+from .lancedb import LanceDBRetriever
+
+__all__ = ["Retriever", "LanceDBRetriever"]
+
+

docetl/retrievers/base.py

@@ -0,0 +1,35 @@
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import Any
+
+
+@dataclass
+class RetrievalResult:
+    """Container for retrieval outputs."""
+
+    docs: list[dict]
+    rendered_context: str
+    meta: dict[str, Any]
+
+
+class Retriever(ABC):
+    """Abstract base class for retrievers."""
+
+    name: str
+
+    def __init__(self, runner, name: str, config: dict[str, Any]):
+        self.runner = runner
+        self.name = name
+        self.config = config
+
+    @abstractmethod
+    def ensure_index(self) -> None:
+        """Create or verify the underlying index/table."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def retrieve(self, context: dict[str, Any]) -> RetrievalResult:
+        """Execute retrieval based on the provided Jinja context."""
+        raise NotImplementedError

docetl/retrievers/lancedb.py

@@ -0,0 +1,358 @@
+from __future__ import annotations
+
+import json
+import os
+import threading
+from functools import lru_cache
+from typing import Any
+
+from jinja2 import Template
+
+from .base import RetrievalResult, Retriever
+
+
+class LanceDBRetriever(Retriever):
+    """OSS LanceDB-backed retriever supporting FTS, vector, and hybrid search."""
+
+    _index_lock = threading.Lock()
+    _ensured = False
+
+    def _connect(self):
+        # Lazy import to avoid hard dependency during linting/tests without install
+        try:
+            import lancedb  # type: ignore
+        except Exception as e:
+            raise ImportError(
+                "lancedb is required for LanceDBRetriever. Please add it to dependencies."
+            ) from e
+        index_dir = self.config["index_dir"]
+        os.makedirs(index_dir, exist_ok=True)
+        return lancedb.connect(index_dir)
+
+    def _table_name(self) -> str:
+        # Stable table name based on retriever name
+        return f"docetl_{self.name}"
+
+    def _iter_dataset_rows(self) -> list[dict]:
+        """Load dataset referenced by this retriever."""
+        dataset_name = self.config["dataset"]
+        # Ensure datasets are loaded
+        if not getattr(self.runner, "datasets", None):
+            self.runner.load()
+        if dataset_name not in self.runner.datasets:
+            raise ValueError(
+                f"Retriever '{self.name}' references unknown dataset '{dataset_name}'."
+            )
+        return self.runner.datasets[dataset_name].load()
+
+    def _render_input_phrase(self, tmpl: str | None, input_obj: dict) -> str:
+        if not tmpl:
+            return ""
+        try:
+            return Template(tmpl).render(input=input_obj)
+        except Exception:
+            return ""
+
+    def _batch_embed(self, texts: list[str]) -> list[list[float]]:
+        model = self.config.get("embedding", {}).get("model")
+        if not model:
+            # If no embedding model, return empty vectors
+            return []
+        # Use DocETL embedding router + cache
+        resp = self.runner.api.gen_embedding(model, json.dumps(texts))
+        # litellm embedding response
+        vectors = [d["embedding"] for d in resp["data"]]
+        return vectors
+
+    def _index_types(self) -> set[str]:
+        idx = self.config.get("index_types", [])
+        if isinstance(idx, str):
+            idx = [idx]
+        idx = set(idx or [])
+        # Interpret hybrid as both
+        if "hybrid" in idx:
+            idx.update({"fts", "embedding"})
+        return idx
+
+    def ensure_index(self) -> None:
+        build_policy = self.config.get("build_index", "if_missing")
+        with self._index_lock:
+            # Only build once per process; 'always' means rebuild once at startup
+            if self._ensured:
+                return
+
+            db = self._connect()
+            table_name = self._table_name()
+            dataset_name = self.config.get("dataset", "<unknown>")
+            idx_types = self._index_types()
+            try:
+                self.runner.console.log(
+                    f"[cyan]LanceDB[/cyan] ensure_index: table='{table_name}', dataset='{dataset_name}', "
+                    f"index_types={sorted(list(idx_types))}, build_policy='{build_policy}'"
+                )
+            except Exception:
+                pass
+            table = None
+            try:
+                existing_names = db.table_names()
+            except Exception as exc:
+                # Surface the real error instead of sidestepping
+                raise RuntimeError(f"Failed to list LanceDB tables: {exc}") from exc
+            if build_policy == "never":
+                try:
+                    self.runner.console.log(
+                        f"[yellow]LanceDB[/yellow] skipping index build (build_index=never) for '{table_name}'"
+                    )
+                except Exception:
+                    pass
+                self._ensured = True
+                return
+            if table_name in existing_names:
+                if build_policy == "always":
+                    try:
+                        self.runner.console.log(
+                            f"[cyan]LanceDB[/cyan] dropping existing table '{table_name}' (build_index=always)"
+                        )
+                    except Exception:
+                        pass
+                    db.drop_table(table_name)
+                else:
+                    # if_missing and table exists -> nothing to do
+                    try:
+                        self.runner.console.log(
+                            f"[green]LanceDB[/green] index exists for '{table_name}', skipping build (build_index=if_missing)"
+                        )
+                    except Exception:
+                        pass
+                    self._ensured = True
+                    return
+
+            if table is None:
+                try:
+                    rows = self._iter_dataset_rows()
+                except Exception:
+                    # Dataset may not be available yet (e.g., produced by a prior step)
+                    try:
+                        self.runner.console.log(
+                            f"[yellow]LanceDB[/yellow] dataset '{dataset_name}' not available; "
+                            f"deferring index build for '{table_name}'"
+                        )
+                    except Exception:
+                        pass
+                    return
+                idx_types = self._index_types()
+
+                # Build per-row phrases for FTS and Embedding
+                fts_index_tmpl = self.config.get("fts", {}).get("index_phrase", None)
+                emb_index_tmpl = self.config.get("embedding", {}).get(
+                    "index_phrase", None
+                )
+
+                fts_texts: list[str] = []
+                if "fts" in idx_types:
+                    fts_texts = [
+                        self._render_input_phrase(fts_index_tmpl, r) for r in rows
+                    ]
+
+                emb_texts: list[str] = []
+                if "embedding" in idx_types:
+                    emb_texts = [
+                        self._render_input_phrase(emb_index_tmpl, r) for r in rows
+                    ]
+                    # If no template, fall back to fts texts
+                    if not any(emb_texts) and fts_texts:
+                        emb_texts = fts_texts
+                # Compute embeddings for embedding index
+                vectors: list[list[float]] = []
+                if "embedding" in idx_types:
+                    # Batch embeddings
+                    batch = 128
+                    for i in range(0, len(emb_texts), batch):
+                        chunk = emb_texts[i : i + batch]
+                        if not chunk:
+                            continue
+                        vectors.extend(self._batch_embed(chunk))
+
+                # Construct records for LanceDB
+                data = []
+                for idx, r in enumerate(rows):
+                    rec = {
+                        "id": r.get("id", idx),
+                        "text": (
+                            fts_texts[idx]
+                            if fts_texts
+                            else (emb_texts[idx] if emb_texts else "")
+                        ),
+                    }
+                    if vectors and idx < len(vectors):
+                        rec["vector"] = vectors[idx]
+                    # keep some fields for rendering
+                    data.append(rec)
+
+                # Create table
+                table = db.create_table(table_name, data=data, mode="overwrite")
+                try:
+                    self.runner.console.log(
+                        f"[green]LanceDB[/green] created table '{table_name}' with {len(data)} rows "
+                        f"(fts={'fts' in idx_types}, embedding={'embedding' in idx_types})"
+                    )
+                except Exception:
+                    pass
+
+                # Create FTS index if fts enabled
+                if "fts" in idx_types:
+                    try:
+                        table.create_fts_index("text")
+                        try:
+                            self.runner.console.log(
+                                f"[green]LanceDB[/green] created FTS index on column 'text' for '{table_name}'"
+                            )
+                        except Exception:
+                            pass
+                    except Exception:
+                        # Index may already exist or backend unavailable; continue
+                        try:
+                            self.runner.console.log(
+                                f"[yellow]LanceDB[/yellow] FTS index creation skipped/failed for '{table_name}'"
+                            )
+                        except Exception:
+                            pass
+
+            self._ensured = True
+            try:
+                self.runner.console.log(
+                    f"[green]LanceDB[/green] index ready for '{table_name}'"
+                )
+            except Exception:
+                pass
+
+    def _render_query_phrase(self, tmpl: str | None, context: dict[str, Any]) -> str:
+        if not tmpl:
+            return ""
+        try:
+            return Template(tmpl).render(**context)
+        except Exception:
+            return ""
+
+    def _select_mode(self) -> str:
+        qmode = self.config.get("query", {}).get("mode", None)
+        if qmode:
+            return qmode
+        # Auto: hybrid if both index types present
+        idx = self._index_types()
+        if "fts" in idx and "embedding" in idx:
+            return "hybrid"
+        if "fts" in idx:
+            return "fts"
+        if "embedding" in idx:
+            return "embedding"
+        return "fts"
+
+    def _reranker(self):
+        # Lazy import and instantiate RRFReranker when needed
+        try:
+            from lancedb.rerankers import RRFReranker  # type: ignore
+
+            rr = RRFReranker()
+            return rr
+        except Exception:
+            return None
+
+    def _limit_and_format(self, rows: list[dict]) -> list[dict]:
+        top_k = int(self.config.get("query", {}).get("top_k", 5))
+        return rows[:top_k]
+
+    def _fetch(self, context: dict[str, Any]) -> list[dict]:
+        db = self._connect()
+        table = db.open_table(self._table_name())
+        mode = self._select_mode()
+        top_k = int(self.config.get("query", {}).get("top_k", 5))
+
+        # Build queries
+        text_query = self._render_query_phrase(
+            self.config.get("fts", {}).get("query_phrase", None), context
+        )
+        vector_query: list[float] | None = None
+        emb_qtext = self._render_query_phrase(
+            self.config.get("embedding", {}).get("query_phrase", None), context
+        )
+        if emb_qtext:
+            vecs = self._batch_embed([emb_qtext])
+            vector_query = vecs[0] if vecs else None
+
+        # Execute
+        try:
+            if mode == "hybrid" and text_query and vector_query is not None:
+                q = (
+                    table.search(query_type="hybrid")
+                    .vector(vector_query)
+                    .text(text_query)
+                )
+                rr = self._reranker()
+                if rr:
+                    q = q.rerank(rr)
+                df = q.limit(top_k).to_pandas()
+            elif mode == "fts" and text_query:
+                # FTS-only
+                q = table.search(text_query, query_type="fts", fts_columns="text")
+                df = q.limit(top_k).to_pandas()
+            elif mode == "embedding" and vector_query is not None:
+                q = table.search(vector_query)
+                df = q.limit(top_k).to_pandas()
+            else:
+                return []
+        except Exception:
+            # Fallbacks
+            try:
+                q = (
+                    table.search(text_query, query_type="fts", fts_columns="text")
+                    if text_query
+                    else table.search(vector_query)
+                )
+                df = q.limit(top_k).to_pandas()
+            except Exception:
+                return []
+
+        # Convert to list of dicts
+        rows = df.to_dict(orient="records") if hasattr(df, "to_dict") else []
+        return self._limit_and_format(rows)
+
+    def _render_docs(self, docs: list[dict]) -> str:
+        # Minimal, opinionated rendering: bullets of text (truncated)
+        max_chars = 1000
+        lines: list[str] = []
+        for i, d in enumerate(docs, start=1):
+            snippet = str(d.get("text", ""))[:max_chars]
+            if snippet:
+                lines.append(f"- [{i}] {snippet}")
+        return "\n".join(lines)
+
+    @staticmethod
+    @lru_cache(maxsize=256)
+    def _cache_key(name: str, ctx_key: str) -> str:
+        return f"{name}:{ctx_key}"
+
+    def _ctx_fingerprint(self, context: dict[str, Any]) -> str:
+        # Make a stable string key from context, using only parts used by templates
+        if "input" in context:
+            base = context["input"]
+        elif "reduce_key" in context:
+            base = {
+                "reduce_key": context.get("reduce_key", {}),
+                "inputs_len": len(context.get("inputs", [])),
+            }
+        else:
+            base = context
+        try:
+            return json.dumps(base, sort_keys=True, ensure_ascii=False)[:2000]
+        except Exception:
+            return str(base)[:2000]
+
+    def retrieve(self, context: dict[str, Any]) -> RetrievalResult:
+        self.ensure_index()
+        # Build a fingerprint (reserved for future caching)
+        _ = self._ctx_fingerprint(context)
+        docs = self._fetch(context)
+        rendered = self._render_docs(docs) if docs else "No extra context available."
+        meta = {"retriever": self.name, "num_docs": len(docs)}
+        return RetrievalResult(docs=docs, rendered_context=rendered, meta=meta)

docetl/runner.py

@@ -93,6 +93,7 @@ class DSLRunner(ConfigWrapper):
             config: dict[str, Any] | None
             parsing_tools: list[schemas.ParsingTool] | None
             datasets: dict[str, schemas.Dataset]
+            retrievers: dict[str, Any] | None
             operations: list[OpType]
             pipeline: schemas.PipelineSpec
 
@@ -119,6 +120,7 @@ class DSLRunner(ConfigWrapper):
         self.total_cost = 0
         self._initialize_state()
         self._setup_parsing_tools()
+        self._setup_retrievers()
         self._build_operation_graph(config)
         self._compute_operation_hashes()
 
@@ -140,6 +142,31 @@ class DSLRunner(ConfigWrapper):
             self.config.get("parsing_tools", None)
         )
 
+    def _setup_retrievers(self) -> None:
+        """Instantiate retrievers from configuration (lazy index creation)."""
+        from docetl.retrievers.lancedb import LanceDBRetriever
+
+        self.retrievers: dict[str, Any] = {}
+        retrievers_cfg = self.config.get("retrievers", {}) or {}
+        for name, rconf in retrievers_cfg.items():
+            if not isinstance(rconf, dict):
+                raise ValueError(f"Invalid retriever '{name}' configuration")
+            if rconf.get("type") != "lancedb":
+                raise ValueError(
+                    f"Unsupported retriever type '{rconf.get('type')}' for '{name}'. Only 'lancedb' is supported."
+                )
+            required = ["dataset", "index_dir", "index_types"]
+            for key in required:
+                if key not in rconf:
+                    raise ValueError(
+                        f"Retriever '{name}' missing required key '{key}'."
+                    )
+            # Defaults
+            rconf.setdefault("query", {"top_k": 5})
+            rconf.setdefault("build_index", "if_missing")
+
+            self.retrievers[name] = LanceDBRetriever(self, name, rconf)
+
     def _build_operation_graph(self, config: dict) -> None:
         """Build the DAG of operations from configuration"""
         self.config = config

docs/operators/extract.md

@@ -140,6 +140,8 @@ This strategy asks the LLM to generate regex patterns matching the desired conte
 | `timeout` | Timeout for LLM calls in seconds | 120 |
 | `skip_on_error` | Continue processing if errors occur | false |
 | `litellm_completion_kwargs` | Additional parameters for LiteLLM calls | {} |
+| `retriever` | Name of a retriever to use for RAG. See [Retrievers](../retrievers.md). | None |
+| `save_retriever_output` | If true, saves the retrieved context to `_<operation_name>_retrieved_context` in the output. | False |
 
 ## Best Practices
 

docs/operators/map.md

@@ -155,6 +155,8 @@ This example demonstrates how the Map operation can transform long, unstructured
 | `pdf_url_key` | If specified, the key in the input that contains the URL of the PDF to process. | None                          |
 | `calibrate` | Improve consistency across documents by using sample data as reference anchors. | False                          |
 | `num_calibration_docs` | Number of documents to use sample and generate outputs for, for calibration. | 10                          |
+| `retriever` | Name of a retriever to use for RAG. See [Retrievers](../retrievers.md). | None                          |
+| `save_retriever_output` | If true, saves the retrieved context to `_<operation_name>_retrieved_context` in the output. | False                          |
 
 Note: If `drop_keys` is specified, `prompt` and `output` become optional parameters.
 

docs/operators/reduce.md

@@ -66,6 +66,8 @@ This Reduce operation processes customer feedback grouped by department:
 | `max_retries_per_timeout` | Maximum number of retries per timeout                                                                  | 2                           |
 | `litellm_completion_kwargs` | Additional parameters to pass to LiteLLM completion calls. | {}                          |
 | `bypass_cache` | If true, bypass the cache for this operation. | False                          |
+| `retriever` | Name of a retriever to use for RAG. See [Retrievers](../retrievers.md). | None                          |
+| `save_retriever_output` | If true, saves the retrieved context to `_<operation_name>_retrieved_context` in the output. | False                          |
 
 ## Advanced Features
 

docs/retrievers.md

@@ -0,0 +1,194 @@
+## Retrievers (LanceDB OSS)
+
+Retrievers let you augment LLM operations with retrieved context from a LanceDB index built over one of your DocETL datasets. You define retrievers once at the top-level, then attach them to any LLM-powered operation using `retriever: <name>`. At runtime, DocETL performs full‑text, vector, or hybrid search and injects the results into your prompt as `{{ retrieval_context }}`.
+
+LanceDB supports built-in full-text search, vector search, and hybrid with RRF reranking. See the official docs: [LanceDB Hybrid Search docs](https://lancedb.com/docs/search/hybrid-search/).
+
+### Key points
+- Always OSS LanceDB (local `index_dir`).
+- A retriever references an existing dataset from the pipeline config.
+- Operations do not override retriever settings. One source of truth = consistency.
+- `{{ retrieval_context }}` is available to your prompt; if not used, DocETL prepends a short “extra context” section automatically.
+
+## Configuration (clear separation of index vs query)
+
+Add a top-level `retrievers` section. Each retriever has:
+- `dataset`: dataset name to index
+- `index_dir`: LanceDB path
+- `index_types`: which indexes to build: `fts`, `embedding`, or `hybrid` (interpreted as both `fts` and `embedding`)
+- `fts.index_phrase`: Jinja for how to index each dataset row for FTS (context: `input`)
+- `fts.query_phrase`: Jinja for how to build the FTS query (context: operation context)
+- `embedding.model`: embedding model used for the vector index and for query vectors
+- `embedding.index_phrase`: Jinja for how to index each dataset row for embedding (context: `input`)
+- `embedding.query_phrase`: Jinja for how to build the embedding query text (context: operation context)
+- `query.mode`: `fts` | `embedding` | `hybrid` (defaults to `hybrid` when both indexes exist)
+- `query.top_k`: number of results to retrieve
+
+```yaml
+datasets:
+  transcripts:
+    type: file
+    path: workloads/medical/raw.json
+
+default_model: gpt-4o-mini
+
+retrievers:
+  medical_r:
+    type: lancedb
+    dataset: transcripts
+    index_dir: workloads/medical/lance_index
+    build_index: if_missing            # if_missing | always | never
+    index_types: ["fts", "embedding"]  # or "hybrid"
+    fts:
+      # How to index each row (context: input == dataset row)
+      index_phrase: >
+        {{ input.src }}
+      # How to build the query (map/filter/extract context: input; reduce: reduce_key & inputs)
+      query_phrase: >
+        {{ input.get("src","")[:1000] if input else "" }}
+    embedding:
+      model: openai/text-embedding-3-small
+      # How to index each row for embedding (context: input == dataset row)
+      index_phrase: >
+        {{ input.src }}
+      # How to build the query text to embed (op context)
+      query_phrase: >
+        {{ input.get("src","")[:1000] if input else "" }}
+    query:
+      mode: hybrid
+      top_k: 8
+```
+
+Notes:
+- Index build is automatic the first time a retriever is used (when `build_index: if_missing`).
+- `fts.index_phrase` and `embedding.index_phrase` are evaluated with `input` for each dataset record (here `input` is the dataset row).
+- `fts.query_phrase` and `embedding.query_phrase` are evaluated with the operation context.
+
+## Configuration reference
+
+Top-level (retrievers.<name>):
+
+| Parameter | Type | Required | Default | Description |
+| --- | --- | --- | --- | --- |
+| type | string | yes | - | Must be `lancedb`. |
+| dataset | string | yes | - | Name of an existing dataset in the pipeline config. |
+| index_dir | string | yes | - | Filesystem path for the LanceDB database. Created if missing. |
+| build_index | enum | no | `if_missing` | `if_missing` \| `always` \| `never`. Controls when to build the index. |
+| index_types | list[string] \| string | yes | - | Which indexes to build: `fts`, `embedding`, or `"hybrid"` (interpreted as both). |
+
+FTS section (retrievers.<name>.fts):
+
+| Parameter | Type | Required | Default | Description |
+| --- | --- | --- | --- | --- |
+| index_phrase | jinja string | required if `fts` in index_types | - | How to index each dataset row. Context: `row`. |
+| query_phrase | jinja string | recommended for FTS/hybrid queries | - | How to construct the FTS query. Context: op context (see below). |
+
+Embedding section (retrievers.<name>.embedding):
+
+| Parameter | Type | Required | Default | Description |
+| --- | --- | --- | --- | --- |
+| model | string | required if `embedding` in index_types | - | Embedding model used for both index vectors and query vectors. |
+| index_phrase | jinja string | no | falls back to `fts.index_phrase` if present | How to index each dataset row for embedding. Context: `row`. |
+| query_phrase | jinja string | recommended for embedding/hybrid queries | - | How to construct the text to embed at query time. Context: op context. |
+
+Query section (retrievers.<name>.query):
+
+| Parameter | Type | Required | Default | Description |
+| --- | --- | --- | --- | --- |
+| mode | enum | no | auto | `fts` \| `embedding` \| `hybrid`. If omitted: `hybrid` when both indexes exist, else whichever index exists. |
+| top_k | int | no | 5 | Number of results to return. |
+
+Notes:
+- Hybrid search uses LanceDB’s built-in reranking (RRF) by default.
+- Jinja contexts:
+  - Map / Filter / Extract: `{"input": <current item>}`
+  - Reduce: `{"reduce_key": {...}, "inputs": [items]}`
+- Jinja for indexing uses `{"input": <dataset row>}`
+- Keep query phrases concise; slice long fields, e.g. `{{ input.src[:1000] }}`.
+- The injected `retrieval_context` is truncated conservatively (~1000 chars per doc).
+
+## Using a retriever in operations
+
+Attach the retriever to any LLM-powered op with `retriever: <name>`. Include `{{ retrieval_context }}` in your prompt or let DocETL prepend it automatically.
+
+### Operation Parameters
+
+When using a retriever with an operation, the following additional parameters are available:
+
+| Parameter | Type | Default | Description |
+| --- | --- | --- | --- |
+| retriever | string | - | Name of the retriever to use (must be defined in the `retrievers` section). |
+| save_retriever_output | bool | false | If true, saves the retrieved context to `_<operation_name>_retrieved_context` in the output. Useful for debugging and verifying retrieval quality. |
+
+### Map
+```yaml
+operations:
+  - name: tag_visit
+    type: map
+    retriever: medical_r
+    save_retriever_output: true  # Optional: save retrieved context to output
+    output:
+      schema:
+        tag: string
+        confidence: float
+    prompt: |
+      Classify the medical visit. Use the extra context if helpful:
+      {{ retrieval_context }}
+      Transcript:
+      {{ input.src }}
+```
+
+When `save_retriever_output: true`, each output document will include a `_tag_visit_retrieved_context` field containing the exact context that was retrieved and used for that document.
+
+### Extract
+```yaml
+  - name: extract_side_effects
+    type: extract
+    retriever: medical_r
+    document_keys: ["src"]
+    prompt: "Extract side effects mentioned in the text."
+```
+
+### Filter
+```yaml
+  - name: filter_relevant
+    type: filter
+    retriever: medical_r
+    prompt: "Is this transcript relevant to medication counseling? Return is_relevant: boolean."
+    output:
+      schema:
+        is_relevant: bool
+        _short_explanation: string
+```
+
+### Reduce
+When using reduce, the retrieval context is computed per group. The Jinja context provides both `reduce_key` and `inputs`.
+```yaml
+  - name: summarize_by_medication
+    type: reduce
+    retriever: medical_r
+    reduce_key: "medication"
+    output:
+      schema:
+        summary: string
+    prompt: |
+      Summarize key points for medication '{{ reduce_key.medication }}'.
+      Use the extra context if helpful:
+      {{ retrieval_context }}
+      Inputs:
+      {{ inputs }}
+```
+
+## Jinja template contexts
+- Map / Filter / Extract: `{"input": current_item}`
+- Reduce: `{"reduce_key": {...}, "inputs": [items]}`
+
+## Token budget and truncation
+- DocETL uses a conservative default to limit the size of `retrieval_context` by truncating each retrieved text to ~1000 characters.
+
+## Troubleshooting
+- No results: the retriever injects “No extra context available.” and continues.
+- Index issues: set `build_index: always` to rebuild; ensure `index_dir` exists and is writable.
+- Embeddings: DocETL uses its embedding router and caches results where possible.
+
+

mkdocs.yml

@@ -23,6 +23,9 @@ nav:
           - Operators & Validation: concepts/operators.md
           - Output Schemas: concepts/schemas.md
           - Pipelines: concepts/pipelines.md
+          - Optimization: concepts/optimization.md
+      - Retrieval:
+          - Retrievers: retrievers.md
       - LLM-Powered Operators:
           - Map: operators/map.md
           - Resolve: operators/resolve.md

pyproject.toml

@@ -37,7 +37,9 @@ dependencies = [
   "modal>=0.64.0",
 ]
 
-# Optional extras mapped from Poetry extras and optional deps
+[tool.uv]
+managed = true
+
 [project.optional-dependencies]
 parsing = [
   "python-docx>=1.1.2",
@@ -56,6 +58,9 @@ server = [
   "azure-ai-documentintelligence>=1.0.0b4",
   "httpx>=0.27.2",
 ]
+retrieval = [
+  "lancedb>=0.7.0",
+]
 
 [project.scripts]
 docetl = "docetl.cli:app"

tests/basic/test_retriever_lancedb.py

@@ -0,0 +1,317 @@
+import os
+import pytest
+
+from docetl.runner import DSLRunner
+
+
+pytest.importorskip("lancedb")
+
+
+def test_lancedb_retriever_fts_only(tmp_path):
+    kb = [
+        {"id": 1, "text": "alpha beta"},
+        {"id": 2, "text": "gamma delta"},
+        {"id": 3, "text": "epsilon zeta"},
+    ]
+
+    config = {
+        "default_model": "gpt-4o-mini",
+        "datasets": {
+            "kb": {
+                "type": "memory",
+                "path": kb,
+            },
+        },
+        "retrievers": {
+            "kb_r": {
+                "type": "lancedb",
+                "dataset": "kb",
+                "index_dir": str(tmp_path / "idx"),
+                "build_index": "always",
+                "index_types": ["fts"],
+                "fts": {
+                    "index_phrase": "{{ input.text }}",
+                    "query_phrase": "{{ input.q }}",
+                },
+                "query": {"top_k": 2, "mode": "fts"},
+            }
+        },
+        "operations": [],
+        "pipeline": {"steps": [], "output": {"path": str(tmp_path / "out.json")}},
+    }
+
+    runner = DSLRunner(config, max_threads=8)
+    # Ensure datasets loaded for retriever indexing
+    runner.load()
+    r = runner.retrievers["kb_r"]
+    r.ensure_index()
+
+    res = r.retrieve({"input": {"q": "alpha"}})
+    assert res.docs, "Expected at least one FTS hit"
+    ctx = res.rendered_context
+    assert "alpha" in ctx, f"retrieval_context should include indexed text, got: {ctx}"
+
+@pytest.mark.skipif(
+    "OPENAI_API_KEY" not in os.environ,
+    reason="Requires OpenAI embeddings (set OPENAI_API_KEY)",
+)
+def test_lancedb_retriever_embedding_only(tmp_path):
+    kb = [
+        {"id": 1, "text": "alpha beta"},
+        {"id": 2, "text": "gamma delta"},
+        {"id": 3, "text": "epsilon zeta"},
+    ]
+
+    config = {
+        "default_model": "gpt-4o-mini",
+        "datasets": {
+            "kb": {
+                "type": "memory",
+                "path": kb,
+            },
+        },
+        "retrievers": {
+            "kb_r": {
+                "type": "lancedb",
+                "dataset": "kb",
+                "index_dir": str(tmp_path / "idx2"),
+                "build_index": "always",
+                "index_types": ["embedding"],
+                "embedding": {
+                    "model": "text-embedding-3-small",
+                    "index_phrase": "{{ input.text }}",
+                    "query_phrase": "{{ input.q }}",
+                },
+                "query": {"top_k": 2, "mode": "embedding"},
+            }
+        },
+        "operations": [],
+        "pipeline": {"steps": [], "output": {"path": str(tmp_path / "out2.json")}},
+    }
+
+    runner = DSLRunner(config, max_threads=8)
+    runner.load()
+    r = runner.retrievers["kb_r"]
+    r.ensure_index()
+
+    res = r.retrieve({"input": {"q": "alpha"}})
+    assert res.docs, "Expected at least one embedding hit"
+    assert "alpha" in res.rendered_context or "beta" in res.rendered_context
+
+
+@pytest.mark.skipif(
+    "OPENAI_API_KEY" not in os.environ,
+    reason="Requires OpenAI embeddings (set OPENAI_API_KEY)",
+)
+def test_lancedb_retriever_hybrid(tmp_path):
+    kb = [
+        {"id": 1, "text": "alpha beta"},
+        {"id": 2, "text": "gamma delta"},
+        {"id": 3, "text": "epsilon zeta"},
+    ]
+
+    config = {
+        "default_model": "gpt-4o-mini",
+        "datasets": {
+            "kb": {
+                "type": "memory",
+                "path": kb,
+            },
+        },
+        "retrievers": {
+            "kb_r": {
+                "type": "lancedb",
+                "dataset": "kb",
+                "index_dir": str(tmp_path / "idx3"),
+                "build_index": "always",
+                "index_types": ["fts", "embedding"],
+                "fts": {
+                    "index_phrase": "{{ input.text }}",
+                    "query_phrase": "{{ input.q }}",
+                },
+                "embedding": {
+                    "model": "text-embedding-3-small",
+                    "index_phrase": "{{ input.text }}",
+                    "query_phrase": "{{ input.q }}",
+                },
+                "query": {"top_k": 2, "mode": "hybrid"},
+            }
+        },
+        "operations": [],
+        "pipeline": {"steps": [], "output": {"path": str(tmp_path / "out3.json")}},
+    }
+
+    runner = DSLRunner(config, max_threads=8)
+    runner.load()
+    r = runner.retrievers["kb_r"]
+    r.ensure_index()
+
+    res = r.retrieve({"input": {"q": "alpha"}})
+    assert res.docs, "Expected at least one hybrid hit"
+    assert "alpha" in res.rendered_context
+
+
+def test_lancedb_retriever_different_contexts_per_query(tmp_path):
+    """Test that different queries return different retrieval contexts."""
+    kb = [
+        {"id": 1, "text": "The quick brown fox jumps over the lazy dog"},
+        {"id": 2, "text": "Python is a programming language used for data science"},
+        {"id": 3, "text": "Machine learning models require training data"},
+        {"id": 4, "text": "Cats and dogs are popular household pets"},
+    ]
+
+    config = {
+        "default_model": "gpt-4o-mini",
+        "datasets": {
+            "kb": {
+                "type": "memory",
+                "path": kb,
+            },
+        },
+        "retrievers": {
+            "kb_r": {
+                "type": "lancedb",
+                "dataset": "kb",
+                "index_dir": str(tmp_path / "idx_diff"),
+                "build_index": "always",
+                "index_types": ["fts"],
+                "fts": {
+                    "index_phrase": "{{ input.text }}",
+                    "query_phrase": "{{ input.query }}",
+                },
+                "query": {"top_k": 1, "mode": "fts"},
+            }
+        },
+        "operations": [],
+        "pipeline": {"steps": [], "output": {"path": str(tmp_path / "out_diff.json")}},
+    }
+
+    runner = DSLRunner(config, max_threads=8)
+    runner.load()
+    r = runner.retrievers["kb_r"]
+    r.ensure_index()
+
+    # Query for "fox" should return the fox document
+    res_fox = r.retrieve({"input": {"query": "fox"}})
+    assert res_fox.docs, "Expected at least one FTS hit for 'fox'"
+    ctx_fox = res_fox.rendered_context
+
+    # Query for "Python" should return the Python document
+    res_python = r.retrieve({"input": {"query": "Python programming"}})
+    assert res_python.docs, "Expected at least one FTS hit for 'Python'"
+    ctx_python = res_python.rendered_context
+
+    # Query for "machine learning" should return the ML document
+    res_ml = r.retrieve({"input": {"query": "machine learning"}})
+    assert res_ml.docs, "Expected at least one FTS hit for 'machine learning'"
+    ctx_ml = res_ml.rendered_context
+
+    # Query for "cats dogs pets" should return the pets document
+    res_pets = r.retrieve({"input": {"query": "cats dogs pets"}})
+    assert res_pets.docs, "Expected at least one FTS hit for 'pets'"
+    ctx_pets = res_pets.rendered_context
+
+    # Verify each context contains the expected content
+    assert "fox" in ctx_fox.lower(), f"Expected 'fox' in context, got: {ctx_fox}"
+    assert (
+        "python" in ctx_python.lower()
+    ), f"Expected 'python' in context, got: {ctx_python}"
+    assert (
+        "machine" in ctx_ml.lower() or "learning" in ctx_ml.lower()
+    ), f"Expected ML terms in context, got: {ctx_ml}"
+    assert (
+        "cats" in ctx_pets.lower() or "dogs" in ctx_pets.lower()
+    ), f"Expected pet terms in context, got: {ctx_pets}"
+
+    # Verify contexts are different from each other
+    contexts = [ctx_fox, ctx_python, ctx_ml, ctx_pets]
+    for i, ctx1 in enumerate(contexts):
+        for j, ctx2 in enumerate(contexts):
+            if i < j:
+                assert (
+                    ctx1 != ctx2
+                ), f"Contexts {i} and {j} should be different but both are: {ctx1}"
+
+
+@pytest.mark.skipif(
+    "OPENAI_API_KEY" not in os.environ,
+    reason="Requires OpenAI API key for LLM calls",
+)
+def test_map_operation_includes_retrieved_context_in_output(tmp_path):
+    """Test that map operation adds _retrieved_context key to output."""
+    kb = [
+        {"id": 1, "text": "Aspirin is used for pain relief and reducing fever"},
+        {"id": 2, "text": "Insulin is used to treat diabetes"},
+        {"id": 3, "text": "Antibiotics fight bacterial infections"},
+    ]
+
+    input_data = [
+        {"id": 1, "symptom": "headache and fever"},
+        {"id": 2, "symptom": "high blood sugar"},
+        {"id": 3, "symptom": "bacterial infection"},
+    ]
+
+    config = {
+        "default_model": "gpt-4o-mini",
+        "datasets": {
+            "kb": {"type": "memory", "path": kb},
+            "symptoms": {"type": "memory", "path": input_data},
+        },
+        "retrievers": {
+            "drug_r": {
+                "type": "lancedb",
+                "dataset": "kb",
+                "index_dir": str(tmp_path / "idx_map"),
+                "build_index": "always",
+                "index_types": ["fts"],
+                "fts": {
+                    "index_phrase": "{{ input.text }}",
+                    "query_phrase": "{{ input.symptom }}",
+                },
+                "query": {"top_k": 1, "mode": "fts"},
+            }
+        },
+        "operations": [
+            {
+                "name": "classify_symptom",
+                "type": "map",
+                "retriever": "drug_r",
+                "save_retriever_output": True,
+                "prompt": "Based on the symptom '{{ input.symptom }}' and context: {{ retrieval_context }}, suggest a treatment category.",
+                "output": {"schema": {"category": "string"}},
+            }
+        ],
+        "pipeline": {
+            "steps": [
+                {"name": "step1", "input": "symptoms", "operations": ["classify_symptom"]}
+            ],
+            "output": {"path": str(tmp_path / "out_map.json")},
+        },
+    }
+
+    runner = DSLRunner(config, max_threads=1)
+    runner.load()
+    results, _, _ = runner.last_op_container.next()
+
+    # Verify that each result has the _retrieved_context key
+    assert len(results) == 3, f"Expected 3 results, got {len(results)}"
+
+    for i, result in enumerate(results):
+        ctx_key = "_classify_symptom_retrieved_context"
+        assert (
+            ctx_key in result
+        ), f"Result {i} missing '{ctx_key}' key. Keys: {list(result.keys())}"
+
+        retrieved_ctx = result[ctx_key]
+        assert retrieved_ctx, f"Result {i} has empty retrieved context"
+
+    # Verify the retrieved contexts are different for different symptoms
+    ctx1 = results[0]["_classify_symptom_retrieved_context"]
+    ctx2 = results[1]["_classify_symptom_retrieved_context"]
+    ctx3 = results[2]["_classify_symptom_retrieved_context"]
+
+    # Each should have retrieved different content
+    assert ctx1 != ctx2, "Contexts for different symptoms should differ"
+    assert ctx2 != ctx3, "Contexts for different symptoms should differ"
+    assert ctx1 != ctx3, "Contexts for different symptoms should differ"
+

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