diff --git a/.github/labeler.yml b/.github/labeler.yml
index a853e8ab95..12eb28c232 100644
--- a/.github/labeler.yml
+++ b/.github/labeler.yml
@@ -79,6 +79,12 @@ integration:faiss:
- any-glob-to-any-file: "integrations/faiss/**/*"
- any-glob-to-any-file: ".github/workflows/faiss.yml"
+
+integration:falkordb:
+ - changed-files:
+ - any-glob-to-any-file: "integrations/falkordb/**/*"
+ - any-glob-to-any-file: ".github/workflows/falkordb.yml"
+
integration:fastembed:
- changed-files:
- any-glob-to-any-file: "integrations/fastembed/**/*"
diff --git a/.github/workflows/CI_coverage_comment.yml b/.github/workflows/CI_coverage_comment.yml
index 763d02316b..7193590867 100644
--- a/.github/workflows/CI_coverage_comment.yml
+++ b/.github/workflows/CI_coverage_comment.yml
@@ -18,6 +18,7 @@ on:
- "Test / dspy"
- "Test / elasticsearch"
- "Test / faiss"
+ - "Test / falkordb"
- "Test / fastembed"
- "Test / firecrawl"
- "Test / github"
diff --git a/.github/workflows/falkordb.yml b/.github/workflows/falkordb.yml
new file mode 100644
index 0000000000..95feca3f16
--- /dev/null
+++ b/.github/workflows/falkordb.yml
@@ -0,0 +1,155 @@
+# This workflow comes from https://github.com/ofek/hatch-mypyc
+# https://github.com/ofek/hatch-mypyc/blob/5a198c0ba8660494d02716cfc9d79ce4adfb1442/.github/workflows/test.yml
+name: Test / falkordb
+
+on:
+ schedule:
+ - cron: "0 0 * * *"
+ pull_request:
+ paths:
+ - "integrations/falkordb/**"
+ - "!integrations/falkordb/*.md"
+ - ".github/workflows/falkordb.yml"
+ push:
+ branches:
+ - main
+ paths:
+ - "integrations/falkordb/**"
+ - "!integrations/falkordb/*.md"
+ - ".github/workflows/falkordb.yml"
+
+defaults:
+ run:
+ working-directory: integrations/falkordb
+
+concurrency:
+ group: falkordb-${{ github.head_ref || github.sha }}
+ cancel-in-progress: true
+
+env:
+ PYTHONUNBUFFERED: "1"
+ FORCE_COLOR: "1"
+ TEST_MATRIX_OS: '["ubuntu-latest"]'
+ TEST_MATRIX_PYTHON: '["3.10", "3.14"]'
+
+jobs:
+ compute-test-matrix:
+ runs-on: ubuntu-slim
+ defaults:
+ run:
+ working-directory: .
+ outputs:
+ os: ${{ steps.set.outputs.os }}
+ python-version: ${{ steps.set.outputs.python-version }}
+ steps:
+ - id: set
+ run: |
+ echo 'os=${{ github.event_name == 'push' && '["ubuntu-latest"]' || env.TEST_MATRIX_OS }}' >> "$GITHUB_OUTPUT"
+ echo 'python-version=${{ github.event_name == 'push' && '["3.10"]' || env.TEST_MATRIX_PYTHON }}' >> "$GITHUB_OUTPUT"
+
+ run:
+ name: Python ${{ matrix.python-version }} on ${{ startsWith(matrix.os, 'macos-') && 'macOS' || startsWith(matrix.os, 'windows-') && 'Windows' || 'Linux' }}
+ needs: compute-test-matrix
+ permissions:
+ contents: write
+ pull-requests: write
+ runs-on: ${{ matrix.os }}
+ strategy:
+ fail-fast: false
+ matrix:
+ os: ${{ fromJSON(needs.compute-test-matrix.outputs.os) }}
+ python-version: ${{ fromJSON(needs.compute-test-matrix.outputs.python-version) }}
+ services:
+ falkordb:
+ image: falkordb/falkordb:latest
+ ports:
+ - 6379:6379
+ options: >-
+ --health-cmd "redis-cli ping"
+ --health-interval 10s
+ --health-timeout 5s
+ --health-retries 5
+
+ steps:
+ - name: Support longpaths
+ if: matrix.os == 'windows-latest'
+ working-directory: .
+ run: git config --system core.longpaths true
+
+ - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
+
+ - name: Set up Python ${{ matrix.python-version }}
+ uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
+ with:
+ python-version: ${{ matrix.python-version }}
+
+ - name: Install Hatch
+ run: pip install --upgrade hatch
+ - name: Lint
+ if: matrix.python-version == '3.10' && runner.os == 'Linux'
+ run: hatch run fmt-check && hatch run test:types
+
+ - name: Run unit tests
+ run: hatch run test:unit-cov-retry
+
+ # On PR: posts coverage comment (directly on same-repo PRs; via artifact for fork PRs). On push to main: stores coverage baseline on data branch.
+ - name: Store unit tests coverage
+ id: coverage_comment
+ if: matrix.python-version == '3.10' && runner.os == 'Linux' && github.event_name != 'schedule'
+ uses: py-cov-action/python-coverage-comment-action@7188638f871f721a365d644f505d1ff3df20d683 # v3.40
+ with:
+ GITHUB_TOKEN: ${{ github.token }}
+ COVERAGE_PATH: integrations/falkordb
+ SUBPROJECT_ID: falkordb
+ MINIMUM_GREEN: 90
+ MINIMUM_ORANGE: 60
+
+ - name: Upload coverage comment to be posted
+ if: matrix.python-version == '3.10' && runner.os == 'Linux' && github.event_name == 'pull_request' && steps.coverage_comment.outputs.COMMENT_FILE_WRITTEN == 'true'
+ uses: actions/upload-artifact@bbbca2ddaa5d8feaa63e36b76fdaad77386f024f # v7.0.0
+ with:
+ name: coverage-comment-falkordb
+ path: python-coverage-comment-action-falkordb.txt
+
+ - name: Run integration tests
+ # Integration tests require a live FalkorDB instance via Docker.
+ # Docker services are only available on Linux runners.
+ if: runner.os == 'Linux'
+ env:
+ FALKORDB_HOST: localhost
+ FALKORDB_PORT: 6379
+ run: hatch run test:integration-cov-append-retry
+
+ - name: Store combined coverage
+ if: github.event_name == 'push'
+ uses: py-cov-action/python-coverage-comment-action@7188638f871f721a365d644f505d1ff3df20d683 # v3.40
+ with:
+ GITHUB_TOKEN: ${{ github.token }}
+ COVERAGE_PATH: integrations/falkordb
+ SUBPROJECT_ID: falkordb-combined
+ MINIMUM_GREEN: 90
+ MINIMUM_ORANGE: 60
+
+ - name: Run unit tests with lowest direct dependencies
+ if: github.event_name != 'push'
+ run: |
+ hatch run uv pip compile pyproject.toml --resolution lowest-direct --output-file requirements_lowest_direct.txt
+ hatch -e test env run -- uv pip install -r requirements_lowest_direct.txt
+ hatch run test:unit
+
+ - name: Nightly - run unit tests with Haystack main branch
+ if: github.event_name == 'schedule'
+ run: |
+ hatch env prune
+ hatch -e test env run -- uv pip install git+https://github.com/deepset-ai/haystack.git@main
+ hatch run test:unit
+
+
+ notify-slack-on-failure:
+ needs: run
+ if: failure() && github.event_name == 'schedule'
+ runs-on: ubuntu-slim
+ steps:
+ - uses: deepset-ai/notify-slack-action@3cda73b77a148f16f703274198e7771340cf862b # v1
+ with:
+ slack-webhook-url: ${{ secrets.SLACK_WEBHOOK_URL_NOTIFICATIONS }}
diff --git a/CLAUDE.md b/CLAUDE.md
index 8f8a6efba4..15730ee9c4 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -1,3 +1,3 @@
-# CLAUDE.md
+# CLAUDE.md — FalkorDB Haystack Integration: Execution Plan
-Before you start working on this repository, read the AGENTS.md file and follow all the instructions.
+> **⚠️ ALWAYS READ FIRST:** Before working in this repo, read `AGENTS.md` and follow ALL instructions there.
diff --git a/README.md b/README.md
index 8f3de61ed7..7ba9253548 100644
--- a/README.md
+++ b/README.md
@@ -41,6 +41,7 @@ Please check out our [Contribution Guidelines](CONTRIBUTING.md) for all the deta
| [dspy-haystack](integrations/dspy/) | Generator | [](https://pypi.org/project/dspy-haystack) | [](https://github.com/deepset-ai/haystack-core-integrations/actions/workflows/dspy.yml) | [](https://htmlpreview.github.io/?https://github.com/deepset-ai/haystack-core-integrations/blob/python-coverage-comment-action-data-dspy/htmlcov/index.html) | [](https://htmlpreview.github.io/?https://github.com/deepset-ai/haystack-core-integrations/blob/python-coverage-comment-action-data-dspy-combined/htmlcov/index.html) |
| [elasticsearch-haystack](integrations/elasticsearch/) | Document Store | [](https://pypi.org/project/elasticsearch-haystack) | [](https://github.com/deepset-ai/haystack-core-integrations/actions/workflows/elasticsearch.yml) | [](https://htmlpreview.github.io/?https://github.com/deepset-ai/haystack-core-integrations/blob/python-coverage-comment-action-data-elasticsearch/htmlcov/index.html) | [](https://htmlpreview.github.io/?https://github.com/deepset-ai/haystack-core-integrations/blob/python-coverage-comment-action-data-elasticsearch-combined/htmlcov/index.html) |
| [faiss-haystack](integrations/faiss/) | Document Store | [](https://pypi.org/project/faiss-haystack) | [](https://github.com/deepset-ai/haystack-core-integrations/actions/workflows/faiss.yml) | [](https://htmlpreview.github.io/?https://github.com/deepset-ai/haystack-core-integrations/blob/python-coverage-comment-action-data-faiss/htmlcov/index.html) | [](https://htmlpreview.github.io/?https://github.com/deepset-ai/haystack-core-integrations/blob/python-coverage-comment-action-data-faiss-combined/htmlcov/index.html) |
+| [falkordb-haystack](integrations/falkordb/) | Document Store | [](https://pypi.org/project/falkordb-haystack) | [](https://github.com/deepset-ai/haystack-core-integrations/actions/workflows/falkordb.yml) | [](https://htmlpreview.github.io/?https://github.com/deepset-ai/haystack-core-integrations/blob/python-coverage-comment-action-data-falkordb/htmlcov/index.html) | [](https://htmlpreview.github.io/?https://github.com/deepset-ai/haystack-core-integrations/blob/python-coverage-comment-action-data-falkordb-combined/htmlcov/index.html) |
| [fastembed-haystack](integrations/fastembed/) | Embedder, Ranker | [](https://pypi.org/project/fastembed-haystack/) | [](https://github.com/deepset-ai/haystack-core-integrations/actions/workflows/fastembed.yml) | [](https://htmlpreview.github.io/?https://github.com/deepset-ai/haystack-core-integrations/blob/python-coverage-comment-action-data-fastembed/htmlcov/index.html) | [](https://htmlpreview.github.io/?https://github.com/deepset-ai/haystack-core-integrations/blob/python-coverage-comment-action-data-fastembed-combined/htmlcov/index.html) |
| [firecrawl-haystack](integrations/firecrawl/) | Fetcher, Web Search | [](https://pypi.org/project/firecrawl-haystack/) | [](https://github.com/deepset-ai/haystack-core-integrations/actions/workflows/firecrawl.yml) | [](https://htmlpreview.github.io/?https://github.com/deepset-ai/haystack-core-integrations/blob/python-coverage-comment-action-data-firecrawl/htmlcov/index.html) | [](https://htmlpreview.github.io/?https://github.com/deepset-ai/haystack-core-integrations/blob/python-coverage-comment-action-data-firecrawl-combined/htmlcov/index.html) |
| [github-haystack](integrations/github/) | Connector | [](https://pypi.org/project/github-haystack) | [](https://github.com/deepset-ai/haystack-core-integrations/actions/workflows/github.yml) | [](https://htmlpreview.github.io/?https://github.com/deepset-ai/haystack-core-integrations/blob/python-coverage-comment-action-data-github/htmlcov/index.html) | |
diff --git a/integrations/falkordb/LICENSE.txt b/integrations/falkordb/LICENSE.txt
new file mode 100644
index 0000000000..6134ab324f
--- /dev/null
+++ b/integrations/falkordb/LICENSE.txt
@@ -0,0 +1,201 @@
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+ 1. Definitions.
+
+ "License" shall mean the terms and conditions for use, reproduction,
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+ 5. Submission of Contributions. Unless You explicitly state otherwise,
+ any Contribution intentionally submitted for inclusion in the Work
+ by You to the Licensor shall be under the terms and conditions of
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+ Notwithstanding the above, nothing herein shall supersede or modify
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+ 6. Trademarks. This License does not grant permission to use the trade
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+ 7. Disclaimer of Warranty. Unless required by applicable law or
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+ 8. Limitation of Liability. In no event and under no legal theory,
+ whether in tort (including negligence), contract, or otherwise,
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+ 9. Accepting Warranty or Additional Liability. While redistributing
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+ of your accepting any such warranty or additional liability.
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+ END OF TERMS AND CONDITIONS
+
+ APPENDIX: How to apply the Apache License to your work.
+
+ To apply the Apache License to your work, attach the following
+ boilerplate notice, with the fields enclosed by brackets "[]"
+ replaced with your own identifying information. (Don't include
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+ Copyright 2023-present deepset GmbH
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
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+ See the License for the specific language governing permissions and
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diff --git a/integrations/falkordb/README.md b/integrations/falkordb/README.md
new file mode 100644
index 0000000000..11b7ba0575
--- /dev/null
+++ b/integrations/falkordb/README.md
@@ -0,0 +1,23 @@
+# falkordb-haystack
+
+[](https://pypi.org/project/falkordb-haystack)
+[](https://pypi.org/project/falkordb-haystack)
+
+- [Changelog](https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/falkordb/CHANGELOG.md)
+
+---
+
+
+## Contributing
+
+Refer to the general [Contribution Guidelines](https://github.com/deepset-ai/haystack-core-integrations/blob/main/CONTRIBUTING.md).
+
+
+
+## Testing
+
+To run the integration tests locally, you need a running FalkorDB instance. You can start one using the provided docker-compose file:
+
+```bash
+docker-compose up -d
+```
\ No newline at end of file
diff --git a/integrations/falkordb/TEST_FIX_PLAN.md b/integrations/falkordb/TEST_FIX_PLAN.md
new file mode 100644
index 0000000000..aebb7f05a4
--- /dev/null
+++ b/integrations/falkordb/TEST_FIX_PLAN.md
@@ -0,0 +1,371 @@
+# FalkorDB Integration — Test Fix Plan
+
+**Date**: 2026-04-20
+**Branch**: `feature/falkordb-integration`
+**Test command**: `hatch run test:integration` (run from `integrations/falkordb/`)
+**Current result**: 28 failed / 21 passed out of 49 tests
+
+---
+
+## 1. Overview of All Failures
+
+```
+FAILED test_comparison_equal_with_none
+FAILED test_comparison_not_equal
+FAILED test_comparison_not_equal_with_none
+FAILED test_comparison_greater_than
+FAILED test_comparison_greater_than_with_iso_date
+FAILED test_comparison_greater_than_with_string ← expects FilterError, none raised
+FAILED test_comparison_greater_than_with_list ← expects FilterError, none raised
+FAILED test_comparison_greater_than_with_none ← raises FilterError, should return []
+FAILED test_comparison_greater_than_equal
+FAILED test_comparison_greater_than_equal_with_iso_date
+FAILED test_comparison_greater_than_equal_with_string ← expects FilterError, none raised
+FAILED test_comparison_greater_than_equal_with_list ← expects FilterError, none raised
+FAILED test_comparison_greater_than_equal_with_none ← raises FilterError, should return []
+FAILED test_comparison_less_than
+FAILED test_comparison_less_than_with_string ← expects FilterError, none raised
+FAILED test_comparison_less_than_with_list ← expects FilterError, none raised
+FAILED test_comparison_less_than_with_none ← raises FilterError, should return []
+FAILED test_comparison_less_than_equal
+FAILED test_comparison_less_than_equal_with_iso_date
+FAILED test_comparison_less_than_equal_with_string ← expects FilterError, none raised
+FAILED test_comparison_less_than_equal_with_list ← expects FilterError, none raised
+FAILED test_comparison_less_than_equal_with_none ← raises FilterError, should return []
+FAILED test_comparison_in
+FAILED test_comparison_in_with_with_non_list_iterable ← expects FilterError, none raised
+FAILED test_comparison_not_in
+FAILED test_comparison_not_in_with_with_non_list_iterable ← expects FilterError, none raised
+FAILED test_or_operator
+FAILED test_not_operator
+```
+
+All failures fall into **three root causes**.
+
+---
+
+## 2. Root Causes
+
+### Root Cause A — `assert_documents_are_equal` ordering + float32 embedding precision
+
+**Affects**: 20 out of 28 failures
+(all the `AssertionError` failures: `test_comparison_equal_with_none`,
+`test_comparison_not_equal`, `test_comparison_not_equal_with_none`,
+`test_comparison_greater_than`, `test_comparison_greater_than_with_iso_date`,
+`test_comparison_greater_than_equal`, `test_comparison_greater_than_equal_with_iso_date`,
+`test_comparison_less_than`, `test_comparison_less_than_equal`,
+`test_comparison_less_than_equal_with_iso_date`,
+`test_comparison_in`, `test_comparison_not_in`,
+`test_or_operator`, `test_not_operator`, and more.)
+
+**Sub-cause A1 — Wrong ordering**
+
+`filter_documents()` returns nodes with `ORDER BY d.id` (lexicographic order of SHA-256
+hashes). The base test's `assert_documents_are_equal` does a direct `received == expected`
+comparison where `expected` is in `filterable_docs` insertion order. These two orderings
+differ for any result set with more than one document.
+
+**Sub-cause A2 — float32 embedding precision loss**
+
+`filterable_docs` uses `_random_embeddings(768)` which returns Python `float` (float64)
+lists. The write path stores embeddings with:
+```cypher
+SET d.embedding = vecf32(doc.embedding)
+```
+`vecf32` truncates 64-bit floats to 32-bit. When read back through the FalkorDB Python
+client, the values are `float32`, which are **not bit-for-bit equal** to the original
+`float64` values. Because Python dataclasses use `==` for all fields, `Document.__eq__`
+fails for any document that has a random embedding.
+
+**Why some tests PASS despite this:**
+- `test_comparison_equal` (number == 100): no document has `number=100` → both lists are
+ empty → `[] == []`.
+- `test_and_operator` (number == 100 AND name == "name_0"): same.
+- `test_no_filters`: writes a single `Document(content="test doc")` with no embedding.
+- `test_comparison_less_than_with_iso_date`: no document has a date *before*
+ `"1969-07-21T20:17:40"` → both lists are empty.
+
+**Fix for Root Cause A**
+
+Override `assert_documents_are_equal` in `TestDocumentStore` to:
+1. Sort both lists by `doc.id` before comparing.
+2. Compare `id`, `content`, `meta`, and whether `embedding` is `None` vs not-`None`
+ (skip exact float comparison since float32 precision loss is inherent to vecf32
+ storage and is not a bug in the filter logic being tested).
+
+---
+
+### Root Cause B — `>`, `>=`, `<`, `<=` operators: wrong behaviour for `None`, `str`, and `list` values
+
+**Affects**: 8 failures
+(`_with_none` ×4, `_with_string` ×4, `_with_list` ×4 — see table below)
+
+| Test | Value | Expected behaviour | Actual behaviour |
+|------|-------|-------------------|-----------------|
+| `test_comparison_greater_than_with_none` | `None` | return `[]` | raises `FilterError` |
+| `test_comparison_greater_than_equal_with_none` | `None` | return `[]` | raises `FilterError` |
+| `test_comparison_less_than_with_none` | `None` | return `[]` | raises `FilterError` |
+| `test_comparison_less_than_equal_with_none` | `None` | return `[]` | raises `FilterError` |
+| `test_comparison_greater_than_with_string` | `"1"` | raise `FilterError` | no error |
+| `test_comparison_greater_than_equal_with_string` | `"1"` | raise `FilterError` | no error |
+| `test_comparison_less_than_with_string` | `"1"` | raise `FilterError` | no error |
+| `test_comparison_less_than_equal_with_string` | `"1"` | raise `FilterError` | no error |
+| `test_comparison_greater_than_with_list` | `[1]` | raise `FilterError` | no error |
+| `test_comparison_greater_than_equal_with_list` | `[1]` | raise `FilterError` | no error |
+| `test_comparison_less_than_with_list` | `[1]` | raise `FilterError` | no error |
+| `test_comparison_less_than_equal_with_list` | `[1]` | raise `FilterError` | no error |
+
+**Important nuance for strings**: ISO date strings (e.g., `"1972-12-11T19:54:58"`) ARE
+valid values for `>`, `>=`, `<`, `<=`. The tests `test_comparison_greater_than_with_iso_date`,
+`test_comparison_less_than_with_iso_date`, etc. verify that ISO date comparisons work
+correctly. Only non-ISO strings (like `"1"`) must raise `FilterError`.
+
+**Current code** (in `_build_clause`, `document_store.py:664`):
+```python
+if operator in _COMPARISON_OPS:
+ if value is None:
+ msg = f"Operator '{operator}' is not supported for None value"
+ raise FilterError(msg) # ← wrong: should return "false"
+ params[param_name] = value # ← wrong: no validation for str/list
+ return f"coalesce(...)
+```
+
+**Fix for Root Cause B**
+
+Replace the `if operator in _COMPARISON_OPS` block with:
+```python
+if operator in _COMPARISON_OPS:
+ if value is None:
+ return "false" # Cypher literal false → empty result set
+ if isinstance(value, list):
+ msg = f"Operator '{operator}' does not support list values"
+ raise FilterError(msg)
+ if isinstance(value, str):
+ try:
+ datetime.fromisoformat(value)
+ except ValueError:
+ msg = (
+ f"Operator '{operator}' requires a numeric or ISO date value, "
+ f"got non-ISO string: '{value}'"
+ )
+ raise FilterError(msg) from None
+ params[param_name] = value
+ return f"coalesce({cypher_field} {_COMPARISON_OPS[operator]} ${param_name}, false)"
+```
+
+Also add at the top of `document_store.py`:
+```python
+from datetime import datetime
+```
+
+---
+
+### Root Cause C — `in` / `not in` operators: non-list iterables silently accepted
+
+**Affects**: 2 failures
+(`test_comparison_in_with_with_non_list_iterable`,
+`test_comparison_not_in_with_with_non_list_iterable`)
+
+Both tests pass a **tuple** as the value (e.g., `(10, 11)`) and expect `FilterError`.
+The current code does `list(value)`, which silently converts tuples → lists with no error.
+
+**Current code** (in `_build_clause`):
+```python
+if operator == "in":
+ ...
+ try:
+ params[param_name] = list(value) # ← silently converts tuples/sets
+ except TypeError as e:
+ raise FilterError(msg) from e
+```
+
+**Fix for Root Cause C**
+
+Replace `list(value)` with a strict `isinstance(value, list)` gate. Only `list` is
+accepted; everything else (int, tuple, set, generator, …) raises `FilterError`.
+
+```python
+if operator == "in":
+ if not isinstance(value, list):
+ msg = f"Operator 'in' requires a list value, got {type(value).__name__}"
+ raise FilterError(msg)
+ params[param_name] = value
+ return f"coalesce({cypher_field} IN ${param_name}, false)"
+
+if operator == "not in":
+ if not isinstance(value, list):
+ msg = f"Operator 'not in' requires a list value, got {type(value).__name__}"
+ raise FilterError(msg)
+ params[param_name] = value
+ return f"coalesce(NOT ({cypher_field} IN ${param_name}), true)"
+```
+
+This replaces both the old `None` guard and the `try/except TypeError` block. The `None`
+case is now handled implicitly: `isinstance(None, list)` is `False` → `FilterError`.
+
+---
+
+## 3. Files to Change
+
+| File | Change type |
+|------|------------|
+| `src/haystack_integrations/document_stores/falkordb/document_store.py` | Bug fix (Root Causes B & C) |
+| `tests/test_document_store.py` | Override `assert_documents_are_equal` (Root Cause A) |
+
+No changes are needed to retrievers, `__init__.py`, or `pyproject.toml`.
+
+---
+
+## 4. Detailed Change Instructions
+
+### 4.1 `document_store.py`
+
+#### Step 1 — Add `datetime` import (line 8, after existing stdlib imports)
+
+```python
+# Before:
+import math
+
+# After:
+import math
+from datetime import datetime
+```
+
+#### Step 2 — Replace the `in` operator block (approximately line 671)
+
+```python
+# REMOVE:
+ if operator == "in":
+ if value is None:
+ msg = "Operator 'in' is not supported for None value"
+ raise FilterError(msg)
+ try:
+ params[param_name] = list(value)
+ except TypeError as e:
+ msg = f"Operator 'in' expects an iterable, but got {type(value)}"
+ raise FilterError(msg) from e
+ return f"coalesce({cypher_field} IN ${param_name}, false)"
+
+# REPLACE WITH:
+ if operator == "in":
+ if not isinstance(value, list):
+ msg = f"Operator 'in' requires a list value, got {type(value).__name__}"
+ raise FilterError(msg)
+ params[param_name] = value
+ return f"coalesce({cypher_field} IN ${param_name}, false)"
+```
+
+#### Step 3 — Replace the `not in` operator block (approximately line 682)
+
+```python
+# REMOVE:
+ if operator == "not in":
+ if value is None:
+ msg = "Operator 'not in' is not supported for None value"
+ raise FilterError(msg)
+ try:
+ params[param_name] = list(value)
+ except TypeError as e:
+ msg = f"Operator 'not in' expects an iterable, but got {type(value)}"
+ raise FilterError(msg) from e
+ return f"coalesce(NOT ({cypher_field} IN ${param_name}), true)"
+
+# REPLACE WITH:
+ if operator == "not in":
+ if not isinstance(value, list):
+ msg = f"Operator 'not in' requires a list value, got {type(value).__name__}"
+ raise FilterError(msg)
+ params[param_name] = value
+ return f"coalesce(NOT ({cypher_field} IN ${param_name}), true)"
+```
+
+#### Step 4 — Replace the `_COMPARISON_OPS` block (approximately line 664)
+
+```python
+# REMOVE:
+ if operator in _COMPARISON_OPS:
+ if value is None:
+ msg = f"Operator '{operator}' is not supported for None value"
+ raise FilterError(msg)
+ params[param_name] = value
+ return f"coalesce({cypher_field} {_COMPARISON_OPS[operator]} ${param_name}, false)"
+
+# REPLACE WITH:
+ if operator in _COMPARISON_OPS:
+ if value is None:
+ return "false"
+ if isinstance(value, list):
+ msg = f"Operator '{operator}' does not support list values"
+ raise FilterError(msg)
+ if isinstance(value, str):
+ try:
+ datetime.fromisoformat(value)
+ except ValueError:
+ msg = (
+ f"Operator '{operator}' requires a numeric or ISO date value, "
+ f"got non-ISO string: '{value}'"
+ )
+ raise FilterError(msg) from None
+ params[param_name] = value
+ return f"coalesce({cypher_field} {_COMPARISON_OPS[operator]} ${param_name}, false)"
+```
+
+---
+
+### 4.2 `tests/test_document_store.py`
+
+Add the `assert_documents_are_equal` override inside `TestDocumentStore`:
+
+```python
+@staticmethod
+def assert_documents_are_equal(received: list[Document], expected: list[Document]):
+ """
+ FalkorDB stores embeddings as vecf32 (float32), so exact float64 comparison
+ is not possible after a round-trip. We compare id, content, and meta fields,
+ and only verify that embedding is None vs not-None. We also sort both lists by
+ document id to compensate for non-deterministic graph traversal order.
+ """
+ assert len(received) == len(expected), (
+ f"Expected {len(expected)} documents but got {len(received)}"
+ )
+ received_sorted = sorted(received, key=lambda d: d.id)
+ expected_sorted = sorted(expected, key=lambda d: d.id)
+ for recv, exp in zip(received_sorted, expected_sorted):
+ assert recv.id == exp.id
+ assert recv.content == exp.content
+ assert recv.meta == exp.meta
+ assert (recv.embedding is None) == (exp.embedding is None), (
+ f"Embedding presence mismatch for doc {recv.id}: "
+ f"received {'None' if recv.embedding is None else 'vector'}, "
+ f"expected {'None' if exp.embedding is None else 'vector'}"
+ )
+```
+
+---
+
+## 5. Verification
+
+After applying all changes, run:
+
+```bash
+cd integrations/falkordb
+hatch run test:integration
+```
+
+Expected outcome: **0 failed, 49 passed**.
+
+Tests that were previously passing must remain passing. The override does not change
+behaviour for:
+- Empty-list comparisons (`[] == []`): `len` check passes, loop doesn't execute.
+- Single-document comparisons with no embedding (write-dup tests): id/content/meta match.
+
+---
+
+## 6. Why No Other Files Need Changes
+
+- `embedding_retriever.py` / `cypher_retriever.py`: not tested by `DocumentStoreBaseTests`.
+- `__init__.py`: exports are correct.
+- `pyproject.toml`: test dependencies and markers are correct.
+- The `_node_to_document` function: correctly pops all standard fields; the embedding
+ round-trip precision loss is an inherent property of vecf32 storage and is documented
+ in the `assert_documents_are_equal` override rather than worked around in the store.
diff --git a/integrations/falkordb/docker-compose.yml b/integrations/falkordb/docker-compose.yml
new file mode 100644
index 0000000000..c12734d9ad
--- /dev/null
+++ b/integrations/falkordb/docker-compose.yml
@@ -0,0 +1,10 @@
+services:
+ falkordb:
+ image: "falkordb/falkordb:latest"
+ ports:
+ - 6379:6379
+ healthcheck:
+ test: ["CMD", "redis-cli", "ping"]
+ interval: 10s
+ timeout: 1s
+ retries: 10
diff --git a/integrations/falkordb/falkordb.md b/integrations/falkordb/falkordb.md
new file mode 100644
index 0000000000..903f27bd93
--- /dev/null
+++ b/integrations/falkordb/falkordb.md
@@ -0,0 +1,365 @@
+---
+title: "FalkorDB"
+id: integrations-falkordb
+description: "FalkorDB integration for Haystack"
+slug: "/integrations-falkordb"
+---
+
+
+## haystack_integrations.components.retrievers.falkordb.cypher_retriever
+
+### FalkorDBCypherRetriever
+
+A power-user retriever for executing arbitrary OpenCypher queries against FalkorDB.
+
+This retriever allows you to leverage graph traversal and multi-hop queries in
+GraphRAG pipelines. The query must return nodes or dictionaries that can be
+mapped exactly to a Haystack `Document`.
+
+**Security Warning:** Raw Cypher queries must only come from trusted sources. Do
+not use un-sanitised user input directly in query strings. Use `parameters` instead.
+
+Usage example:
+
+```python
+from haystack_integrations.document_stores.falkordb import FalkorDBDocumentStore
+from haystack_integrations.components.retrievers.falkordb import FalkorDBCypherRetriever
+
+store = FalkorDBDocumentStore(host="localhost", port=6379)
+retriever = FalkorDBCypherRetriever(
+ document_store=store,
+ custom_cypher_query="MATCH (d:Document)-[:RELATES_TO]->(:Concept {name: $concept}) RETURN d"
+)
+
+res = retriever.run(parameters={"concept": "GraphRAG"})
+print(res["documents"])
+```
+
+#### __init__
+
+```python
+__init__(
+ document_store: FalkorDBDocumentStore,
+ custom_cypher_query: str | None = None,
+) -> None
+```
+
+Create a new FalkorDBCypherRetriever.
+
+**Parameters:**
+
+- **document_store** (FalkorDBDocumentStore) – The FalkorDBDocumentStore instance.
+- **custom_cypher_query** (str | None) – A static OpenCypher query to execute. Can be
+ overridden at runtime by passing `query` to `run()`.
+
+**Raises:**
+
+- ValueError – If the provided `document_store` is not a `FalkorDBDocumentStore`.
+
+#### to_dict
+
+```python
+to_dict() -> dict[str, Any]
+```
+
+Serialize this retriever to a dictionary.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> FalkorDBCypherRetriever
+```
+
+Deserialize a retriever from a dictionary.
+
+#### run
+
+```python
+run(
+ query: str | None = None, parameters: dict[str, Any] | None = None
+) -> dict[str, list[Document]]
+```
+
+Retrieve documents by executing an OpenCypher query.
+
+If a `query` is provided here, it overrides the `custom_cypher_query`
+set during initialisation.
+
+**Parameters:**
+
+- **query** (str | None) – Optional OpenCypher query string.
+- **parameters** (dict\[str, Any\] | None) – Optional dictionary of query parameters (referenced as
+ `$param_name` in the Cypher string).
+
+**Returns:**
+
+- dict\[str, list\[Document\]\] – Dictionary containing a `"documents"` key with the retrieved documents.
+
+**Raises:**
+
+- ValueError – If no query string is provided (both here and at init).
+
+## haystack_integrations.components.retrievers.falkordb.embedding_retriever
+
+### FalkorDBEmbeddingRetriever
+
+A component for retrieving documents from a FalkorDBDocumentStore using vector similarity.
+
+The retriever uses FalkorDB's native vector search index to find documents whose embeddings
+are most similar to the provided query embedding.
+
+Usage example:
+
+```python
+from haystack.dataclasses import Document
+from haystack_integrations.document_stores.falkordb import FalkorDBDocumentStore
+from haystack_integrations.components.retrievers.falkordb import FalkorDBEmbeddingRetriever
+
+store = FalkorDBDocumentStore(host="localhost", port=6379)
+store.write_documents([
+ Document(content="GraphRAG is powerful.", embedding=[0.1, 0.2, 0.3]),
+ Document(content="FalkorDB is fast.", embedding=[0.8, 0.9, 0.1]),
+])
+
+retriever = FalkorDBEmbeddingRetriever(document_store=store)
+res = retriever.run(query_embedding=[0.1, 0.2, 0.3])
+print(res["documents"][0].content) # "GraphRAG is powerful."
+```
+
+#### __init__
+
+```python
+__init__(
+ document_store: FalkorDBDocumentStore,
+ filters: dict[str, Any] | None = None,
+ top_k: int = 10,
+ filter_policy: FilterPolicy = FilterPolicy.REPLACE,
+) -> None
+```
+
+Create a new FalkorDBEmbeddingRetriever.
+
+**Parameters:**
+
+- **document_store** (FalkorDBDocumentStore) – The FalkorDBDocumentStore instance.
+- **filters** (dict\[str, Any\] | None) – Optional Haystack filters to narrow down the search space.
+- **top_k** (int) – Maximum number of documents to retrieve.
+- **filter_policy** (FilterPolicy) – Policy to determine how runtime filters are combined with
+ initialization filters.
+
+**Raises:**
+
+- ValueError – If the provided `document_store` is not a `FalkorDBDocumentStore`.
+
+#### to_dict
+
+```python
+to_dict() -> dict[str, Any]
+```
+
+Serialize this retriever to a dictionary.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> FalkorDBEmbeddingRetriever
+```
+
+Deserialize a retriever from a dictionary.
+
+#### run
+
+```python
+run(
+ query_embedding: list[float],
+ filters: dict[str, Any] | None = None,
+ top_k: int | None = None,
+) -> dict[str, list[Document]]
+```
+
+Retrieve documents by vector similarity.
+
+**Parameters:**
+
+- **query_embedding** (list\[float\]) – Query embedding vector.
+- **filters** (dict\[str, Any\] | None) – Optional Haystack filters to be combined with the init filters based
+ on the configured filter policy.
+- **top_k** (int | None) – Maximum number of documents to return. If not provided, the default
+ top_k from initialization is used.
+
+**Returns:**
+
+- dict\[str, list\[Document\]\] – Dictionary containing a `"documents"` key with the retrieved documents.
+
+## haystack_integrations.document_stores.falkordb.document_store
+
+### FalkorDBDocumentStore
+
+Bases: DocumentStore
+
+A Haystack DocumentStore backed by FalkorDB — a high-performance graph database.
+
+Optimised for GraphRAG workloads.
+
+Documents are stored as graph nodes (labelled `Document` by default) in a named
+FalkorDB graph. Document properties, including `meta` fields, are stored
+**flat** at the same level as `id` and `content` — exactly the same layout as
+the `neo4j-haystack` reference integration.
+
+Vector search is performed via FalkorDB's native vector index —
+**no APOC is required**. All bulk writes use `UNWIND` + `MERGE` for safe,
+idiomatic OpenCypher upserts.
+
+Usage example:
+
+```python
+from haystack_integrations.document_stores.falkordb import FalkorDBDocumentStore
+from haystack.dataclasses import Document
+
+store = FalkorDBDocumentStore(host="localhost", port=6379)
+store.write_documents([
+ Document(content="Hello, GraphRAG!", meta={"year": 2024}),
+])
+print(store.count_documents()) # 1
+```
+
+#### __init__
+
+```python
+__init__(
+ *,
+ host: str = "localhost",
+ port: int = 6379,
+ graph_name: str = "haystack",
+ username: str | None = None,
+ password: Secret | None = None,
+ node_label: str = "Document",
+ embedding_dim: int = 768,
+ embedding_field: str = "embedding",
+ similarity: SimilarityFunction = "cosine",
+ write_batch_size: int = 100,
+ recreate_graph: bool = False,
+ verify_connectivity: bool = False
+) -> None
+```
+
+Create a new FalkorDBDocumentStore.
+
+**Parameters:**
+
+- **host** (str) – Hostname of the FalkorDB server.
+- **port** (int) – Port the FalkorDB server listens on.
+- **graph_name** (str) – Name of the FalkorDB graph to use. Each graph is an isolated
+ namespace.
+- **username** (str | None) – Optional username for FalkorDB authentication.
+- **password** (Secret | None) – Optional :class:`haystack.utils.Secret` holding the FalkorDB
+ password. The secret value is resolved lazily on first connection.
+- **node_label** (str) – Label used for document nodes in the graph.
+- **embedding_dim** (int) – Dimensionality of the vector embeddings. Used when
+ creating the vector index.
+- **embedding_field** (str) – Name of the node property that stores the embedding
+ vector.
+- **similarity** (SimilarityFunction) – Similarity function for the vector index. Accepted values
+ are `"cosine"` and `"euclidean"`.
+- **write_batch_size** (int) – Number of documents written per `UNWIND` batch.
+- **recreate_graph** (bool) – When `True` the existing graph (and all its data) is
+ dropped and recreated on initialisation. Useful for tests.
+- **verify_connectivity** (bool) – When `True` a connectivity probe is run
+ immediately in `__init__` — raises if the server is unreachable.
+
+**Raises:**
+
+- ValueError – If `similarity` is not `"cosine"` or `"euclidean"`.
+
+#### to_dict
+
+```python
+to_dict() -> dict[str, Any]
+```
+
+Serialize this document store to a dictionary.
+
+#### from_dict
+
+```python
+from_dict(data: dict[str, Any]) -> FalkorDBDocumentStore
+```
+
+Deserialize a document store from a dictionary.
+
+#### count_documents
+
+```python
+count_documents() -> int
+```
+
+Return the number of documents currently stored in the graph.
+
+**Returns:**
+
+- int – Integer count of document nodes.
+
+#### filter_documents
+
+```python
+filter_documents(filters: dict[str, Any] | None = None) -> list[Document]
+```
+
+Retrieve all documents that match the provided Haystack filters.
+
+**Parameters:**
+
+- **filters** (dict\[str, Any\] | None) – Optional Haystack filter dict. When `None` all documents are
+ returned. For filter syntax see
+ [Metadata filtering](https://docs.haystack.deepset.ai/docs/metadata-filtering)
+
+**Returns:**
+
+- list\[Document\] – List of matching :class:`haystack.dataclasses.Document` objects.
+
+**Raises:**
+
+- ValueError – If the filter dict is malformed.
+
+#### write_documents
+
+```python
+write_documents(
+ documents: list[Document], policy: DuplicatePolicy = DuplicatePolicy.NONE
+) -> int
+```
+
+Write documents to the FalkorDB graph using `UNWIND` + `MERGE` for batching.
+
+Document `meta` fields are stored **flat** at the same level as `id` and
+`content` — no prefix is added. This matches the layout used by the
+`neo4j-haystack` reference integration.
+
+**Parameters:**
+
+- **documents** (list\[Document\]) – List of :class:`haystack.dataclasses.Document` objects.
+- **policy** (DuplicatePolicy) – How to handle documents whose `id` already exists.
+ Defaults to :attr:`DuplicatePolicy.NONE` (treated as FAIL).
+
+**Returns:**
+
+- int – Number of documents written or updated.
+
+**Raises:**
+
+- ValueError – If `documents` contains non-Document elements.
+- DuplicateDocumentError – If `policy` is FAIL / NONE and a duplicate
+ ID is encountered.
+- DocumentStoreError – If any other DB error occurs.
+
+#### delete_documents
+
+```python
+delete_documents(document_ids: list[str]) -> None
+```
+
+Delete documents by their IDs using a single `UNWIND`-based query.
+
+**Parameters:**
+
+- **document_ids** (list\[str\]) – List of document IDs to remove from the graph.
diff --git a/integrations/falkordb/pydoc/config_docusaurus.yml b/integrations/falkordb/pydoc/config_docusaurus.yml
new file mode 100644
index 0000000000..147278bba4
--- /dev/null
+++ b/integrations/falkordb/pydoc/config_docusaurus.yml
@@ -0,0 +1,15 @@
+loaders:
+ - modules:
+ - haystack_integrations.document_stores.falkordb.document_store
+ - haystack_integrations.components.retrievers.falkordb.embedding_retriever
+ - haystack_integrations.components.retrievers.falkordb.cypher_retriever
+ search_path: [../src]
+processors:
+ - type: filter
+ documented_only: true
+ skip_empty_modules: true
+renderer:
+ description: FalkorDB integration for Haystack
+ id: integrations-falkordb
+ filename: falkordb.md
+ title: FalkorDB
diff --git a/integrations/falkordb/pyproject.toml b/integrations/falkordb/pyproject.toml
new file mode 100644
index 0000000000..c19baf4dea
--- /dev/null
+++ b/integrations/falkordb/pyproject.toml
@@ -0,0 +1,171 @@
+[build-system]
+requires = ["hatchling", "hatch-vcs"]
+build-backend = "hatchling.build"
+
+[project]
+name = "falkordb-haystack"
+dynamic = ["version"]
+description = "FalkorDB graph database integration for Haystack — enables GraphRAG with OpenCypher"
+readme = "README.md"
+requires-python = ">=3.10"
+license = "Apache-2.0"
+keywords = ["haystack", "falkordb", "graphrag", "graph", "vector-search", "document-store", "rag", "openCypher", "graph-database"]
+authors = [{ name = "deepset GmbH", email = "info@deepset.ai" }]
+classifiers = [
+ "License :: OSI Approved :: Apache Software License",
+ "Development Status :: 4 - Beta",
+ "Programming Language :: Python",
+ "Programming Language :: Python :: 3.10",
+ "Programming Language :: Python :: 3.11",
+ "Programming Language :: Python :: 3.12",
+ "Programming Language :: Python :: 3.13",
+ "Programming Language :: Python :: 3.14",
+ "Programming Language :: Python :: Implementation :: CPython",
+ "Programming Language :: Python :: Implementation :: PyPy",
+ "Topic :: Database",
+ "Topic :: Scientific/Engineering :: Artificial Intelligence",
+]
+dependencies = ["haystack-ai>=2.26.1", "falkordb>=1.0,<2"]
+
+[project.urls]
+Documentation = "https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/falkordb#readme"
+Issues = "https://github.com/deepset-ai/haystack-core-integrations/issues"
+Source = "https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/falkordb"
+
+[tool.hatch.build.targets.wheel]
+packages = ["src/haystack_integrations"]
+
+[tool.hatch.version]
+source = "vcs"
+tag-pattern = 'integrations\/falkordb-v(?P.*)'
+
+[tool.hatch.version.raw-options]
+root = "../.."
+git_describe_command = 'git describe --tags --match="integrations/falkordb-v[0-9]*"'
+
+[tool.hatch.envs.default]
+installer = "uv"
+dependencies = ["haystack-pydoc-tools", "ruff"]
+
+[tool.hatch.envs.default.scripts]
+docs = ["haystack-pydoc pydoc/config_docusaurus.yml"]
+fmt = [
+ "ruff check --fix {args}",
+ "ruff format {args}",
+]
+fmt-check = [
+ "ruff check {args}",
+ "ruff format --check {args}",
+]
+
+[tool.hatch.envs.test]
+dependencies = [
+ "pytest",
+ "pytest-asyncio",
+ "pytest-cov",
+ "pytest-rerunfailures",
+ "mypy",
+ "pip",
+ "falkordb>=1.0,<2",
+]
+
+[tool.hatch.envs.test.scripts]
+unit = 'pytest -m "not integration" {args:tests}'
+integration = 'pytest -m "integration" {args:tests}'
+all = 'pytest {args:tests}'
+unit-cov-retry = 'pytest --cov=haystack_integrations --reruns 3 --reruns-delay 30 -x -m "not integration" {args:tests}'
+integration-cov-append-retry = 'pytest --cov=haystack_integrations --cov-append --reruns 3 --reruns-delay 30 -x -m "integration" {args:tests}'
+types = "mypy -p haystack_integrations.document_stores.falkordb -p haystack_integrations.components.retrievers.falkordb {args}"
+
+[tool.mypy]
+install_types = true
+non_interactive = true
+check_untyped_defs = true
+disallow_incomplete_defs = true
+
+[tool.ruff]
+line-length = 120
+extend-exclude = ["scratch/"]
+
+[tool.ruff.lint]
+select = [
+ "A",
+ "ANN",
+ "ARG",
+ "B",
+ "C",
+ "D102", # Missing docstring in public method
+ "D103", # Missing docstring in public function
+ "D205", # 1 blank line required between summary line and description
+ "D209", # Closing triple quotes go to new line
+ "D213", # summary lines must be positioned on the second physical line of the docstring
+ "D417", # Missing argument descriptions in the docstring
+ "D419", # Docstring is empty
+ "DTZ",
+ "E",
+ "EM",
+ "F",
+ "I",
+ "ICN",
+ "ISC",
+ "N",
+ "PLC",
+ "PLE",
+ "PLR",
+ "PLW",
+ "Q",
+ "RUF",
+ "S",
+ "T",
+ "TID",
+ "UP",
+ "W",
+ "YTT",
+]
+ignore = [
+ # Allow non-abstract empty methods in abstract base classes
+ "B027",
+ # Allow function calls in argument defaults (common Haystack pattern for Secret.from_env_var)
+ "B008",
+ # Ignore checks for possible passwords
+ "S105",
+ "S106",
+ "S107",
+ # Ignore complexity
+ "C901",
+ "PLR0911",
+ "PLR0912",
+ "PLR0913",
+ "PLR0915",
+ # Allow `Any` type - used legitimately for dynamic types and SDK boundaries
+ "ANN401",
+]
+
+[tool.ruff.lint.isort]
+known-first-party = ["haystack_integrations"]
+
+[tool.ruff.lint.flake8-tidy-imports]
+ban-relative-imports = "parents"
+
+[tool.ruff.lint.per-file-ignores]
+# Tests can use magic values, assertions, relative imports, and don't need type annotations
+"tests/**/*" = ["PLR2004", "S101", "TID252", "D", "ANN"]
+
+[tool.coverage.run]
+source = ["haystack_integrations"]
+branch = true
+parallel = false
+relative_files = true
+
+[tool.coverage.report]
+omit = ["*/tests/*", "*/__init__.py"]
+show_missing = true
+exclude_lines = ["no cov", "if __name__ == .__main__.:", "if TYPE_CHECKING:"]
+
+[tool.pytest.ini_options]
+addopts = "--strict-markers"
+markers = [
+ "integration: integration tests",
+]
+log_cli = true
+asyncio_default_fixture_loop_scope = "function"
diff --git a/integrations/falkordb/requirements_lowest_direct.txt b/integrations/falkordb/requirements_lowest_direct.txt
new file mode 100644
index 0000000000..cbb86c18fd
--- /dev/null
+++ b/integrations/falkordb/requirements_lowest_direct.txt
@@ -0,0 +1,126 @@
+# This file was autogenerated by uv via the following command:
+# uv pip compile pyproject.toml --resolution lowest-direct --output-file requirements_lowest_direct.txt
+annotated-types==0.7.0
+ # via pydantic
+anyio==4.13.0
+ # via
+ # httpx
+ # openai
+attrs==26.1.0
+ # via
+ # jsonschema
+ # referencing
+backoff==2.2.1
+ # via posthog
+certifi==2026.2.25
+ # via
+ # httpcore
+ # httpx
+ # requests
+charset-normalizer==3.4.7
+ # via requests
+colorama==0.4.6
+ # via tqdm
+distro==1.9.0
+ # via
+ # openai
+ # posthog
+docstring-parser==0.18.0
+ # via haystack-ai
+falkordb==1.0.1
+ # via falkordb-haystack (pyproject.toml)
+filetype==1.2.0
+ # via haystack-ai
+h11==0.16.0
+ # via httpcore
+haystack-ai==2.26.1
+ # via
+ # falkordb-haystack (pyproject.toml)
+ # haystack-experimental
+haystack-experimental==0.19.0
+ # via haystack-ai
+httpcore==1.0.9
+ # via httpx
+httpx==0.28.1
+ # via openai
+idna==3.12
+ # via
+ # anyio
+ # httpx
+ # requests
+jinja2==3.1.6
+ # via haystack-ai
+jiter==0.14.0
+ # via openai
+jsonschema==4.26.0
+ # via haystack-ai
+jsonschema-specifications==2025.9.1
+ # via jsonschema
+lazy-imports==1.2.0
+ # via haystack-ai
+markupsafe==3.0.3
+ # via
+ # haystack-ai
+ # jinja2
+more-itertools==11.0.2
+ # via haystack-ai
+networkx==3.6.1
+ # via haystack-ai
+numpy==2.4.4
+ # via haystack-ai
+openai==2.32.0
+ # via haystack-ai
+posthog==7.13.0
+ # via haystack-ai
+pydantic==2.13.3
+ # via
+ # haystack-ai
+ # openai
+pydantic-core==2.46.3
+ # via pydantic
+pyjwt==2.12.1
+ # via redis
+python-dateutil==2.9.0.post0
+ # via
+ # haystack-ai
+ # posthog
+pyyaml==6.0.3
+ # via haystack-ai
+redis==5.3.1
+ # via falkordb
+referencing==0.37.0
+ # via
+ # jsonschema
+ # jsonschema-specifications
+requests==2.33.1
+ # via
+ # haystack-ai
+ # posthog
+rpds-py==0.30.0
+ # via
+ # jsonschema
+ # referencing
+six==1.17.0
+ # via python-dateutil
+sniffio==1.3.1
+ # via openai
+tenacity==9.1.4
+ # via haystack-ai
+tqdm==4.67.3
+ # via
+ # haystack-ai
+ # openai
+typing-extensions==4.15.0
+ # via
+ # anyio
+ # haystack-ai
+ # openai
+ # posthog
+ # pydantic
+ # pydantic-core
+ # referencing
+ # typing-inspection
+typing-inspection==0.4.2
+ # via pydantic
+urllib3==2.6.3
+ # via requests
diff --git a/integrations/falkordb/scratch/check_methods.py b/integrations/falkordb/scratch/check_methods.py
new file mode 100644
index 0000000000..372c1431eb
--- /dev/null
+++ b/integrations/falkordb/scratch/check_methods.py
@@ -0,0 +1,14 @@
+import falkordb
+import os
+
+host = os.environ.get("FALKORDB_HOST", "localhost")
+port = int(os.environ.get("FALKORDB_PORT", "6379"))
+
+try:
+ client = falkordb.FalkorDB(host=host, port=port)
+ print("Client methods:", [m for m in dir(client) if not m.startswith("_")])
+
+ graph = client.select_graph("dummy_test_graph")
+ print("Graph methods:", [m for m in dir(graph) if not m.startswith("_")])
+except Exception as e:
+ print(f"Error connecting: {e}")
diff --git a/integrations/falkordb/src/haystack_integrations/components/retrievers/falkordb/__init__.py b/integrations/falkordb/src/haystack_integrations/components/retrievers/falkordb/__init__.py
new file mode 100644
index 0000000000..1793cca7db
--- /dev/null
+++ b/integrations/falkordb/src/haystack_integrations/components/retrievers/falkordb/__init__.py
@@ -0,0 +1,8 @@
+# SPDX-FileCopyrightText: 2024-present deepset GmbH
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from .cypher_retriever import FalkorDBCypherRetriever
+from .embedding_retriever import FalkorDBEmbeddingRetriever
+
+__all__ = ["FalkorDBCypherRetriever", "FalkorDBEmbeddingRetriever"]
diff --git a/integrations/falkordb/src/haystack_integrations/components/retrievers/falkordb/cypher_retriever.py b/integrations/falkordb/src/haystack_integrations/components/retrievers/falkordb/cypher_retriever.py
new file mode 100644
index 0000000000..19945c4fde
--- /dev/null
+++ b/integrations/falkordb/src/haystack_integrations/components/retrievers/falkordb/cypher_retriever.py
@@ -0,0 +1,102 @@
+# SPDX-FileCopyrightText: 2024-present deepset GmbH
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Any
+
+from haystack import component, default_from_dict, default_to_dict
+from haystack.dataclasses import Document
+
+from haystack_integrations.document_stores.falkordb import FalkorDBDocumentStore
+
+
+@component
+class FalkorDBCypherRetriever:
+ """
+ A power-user retriever for executing arbitrary OpenCypher queries against FalkorDB.
+
+ This retriever allows you to leverage graph traversal and multi-hop queries in
+ GraphRAG pipelines. The query must return nodes or dictionaries that can be
+ mapped exactly to a Haystack `Document`.
+
+ **Security Warning:** Raw Cypher queries must only come from trusted sources. Do
+ not use un-sanitised user input directly in query strings. Use `parameters` instead.
+
+ Usage example:
+ ```python
+ from haystack_integrations.document_stores.falkordb import FalkorDBDocumentStore
+ from haystack_integrations.components.retrievers.falkordb import FalkorDBCypherRetriever
+
+ store = FalkorDBDocumentStore(host="localhost", port=6379)
+ retriever = FalkorDBCypherRetriever(
+ document_store=store,
+ custom_cypher_query="MATCH (d:Document)-[:RELATES_TO]->(:Concept {name: $concept}) RETURN d"
+ )
+
+ res = retriever.run(parameters={"concept": "GraphRAG"})
+ print(res["documents"])
+ ```
+ """
+
+ def __init__(
+ self,
+ document_store: FalkorDBDocumentStore,
+ custom_cypher_query: str | None = None,
+ ) -> None:
+ """
+ Create a new FalkorDBCypherRetriever.
+
+ :param document_store: The FalkorDBDocumentStore instance.
+ :param custom_cypher_query: A static OpenCypher query to execute. Can be
+ overridden at runtime by passing `query` to `run()`.
+ :raises ValueError: If the provided `document_store` is not a `FalkorDBDocumentStore`.
+ """
+ if not isinstance(document_store, FalkorDBDocumentStore):
+ msg = "document_store must be an instance of FalkorDBDocumentStore"
+ raise ValueError(msg)
+
+ self.document_store = document_store
+ self.custom_cypher_query = custom_cypher_query
+
+ def to_dict(self) -> dict[str, Any]:
+ """Serialize this retriever to a dictionary."""
+ return default_to_dict(
+ self,
+ document_store=self.document_store.to_dict(),
+ custom_cypher_query=self.custom_cypher_query,
+ )
+
+ @classmethod
+ def from_dict(cls, data: dict[str, Any]) -> "FalkorDBCypherRetriever":
+ """Deserialize a retriever from a dictionary."""
+ return default_from_dict(cls, data)
+
+ @component.output_types(documents=list[Document])
+ def run(
+ self,
+ query: str | None = None,
+ parameters: dict[str, Any] | None = None,
+ ) -> dict[str, list[Document]]:
+ """
+ Retrieve documents by executing an OpenCypher query.
+
+ If a `query` is provided here, it overrides the `custom_cypher_query`
+ set during initialisation.
+
+ :param query: Optional OpenCypher query string.
+ :param parameters: Optional dictionary of query parameters (referenced as
+ `$param_name` in the Cypher string).
+ :raises ValueError: If no query string is provided (both here and at init).
+ :returns: Dictionary containing a `"documents"` key with the retrieved documents.
+ """
+ cypher = query or self.custom_cypher_query
+ if not cypher:
+ msg = "A Cypher query string must be provided either at init or at runtime."
+ raise ValueError(msg)
+
+ docs = self.document_store._cypher_retrieval(
+ cypher_query=cypher,
+ parameters=parameters,
+ )
+
+ return {"documents": docs}
diff --git a/integrations/falkordb/src/haystack_integrations/components/retrievers/falkordb/embedding_retriever.py b/integrations/falkordb/src/haystack_integrations/components/retrievers/falkordb/embedding_retriever.py
new file mode 100644
index 0000000000..4980e3db14
--- /dev/null
+++ b/integrations/falkordb/src/haystack_integrations/components/retrievers/falkordb/embedding_retriever.py
@@ -0,0 +1,107 @@
+# SPDX-FileCopyrightText: 2024-present deepset GmbH
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Any
+
+from haystack import component, default_from_dict, default_to_dict
+from haystack.dataclasses import Document
+from haystack.document_stores.types import FilterPolicy, apply_filter_policy
+
+from haystack_integrations.document_stores.falkordb import FalkorDBDocumentStore
+
+
+@component
+class FalkorDBEmbeddingRetriever:
+ """
+ A component for retrieving documents from a FalkorDBDocumentStore using vector similarity.
+
+ The retriever uses FalkorDB's native vector search index to find documents whose embeddings
+ are most similar to the provided query embedding.
+
+ Usage example:
+ ```python
+ from haystack.dataclasses import Document
+ from haystack_integrations.document_stores.falkordb import FalkorDBDocumentStore
+ from haystack_integrations.components.retrievers.falkordb import FalkorDBEmbeddingRetriever
+
+ store = FalkorDBDocumentStore(host="localhost", port=6379)
+ store.write_documents([
+ Document(content="GraphRAG is powerful.", embedding=[0.1, 0.2, 0.3]),
+ Document(content="FalkorDB is fast.", embedding=[0.8, 0.9, 0.1]),
+ ])
+
+ retriever = FalkorDBEmbeddingRetriever(document_store=store)
+ res = retriever.run(query_embedding=[0.1, 0.2, 0.3])
+ print(res["documents"][0].content) # "GraphRAG is powerful."
+ ```
+ """
+
+ def __init__(
+ self,
+ document_store: FalkorDBDocumentStore,
+ filters: dict[str, Any] | None = None,
+ top_k: int = 10,
+ filter_policy: FilterPolicy = FilterPolicy.REPLACE,
+ ) -> None:
+ """
+ Create a new FalkorDBEmbeddingRetriever.
+
+ :param document_store: The FalkorDBDocumentStore instance.
+ :param filters: Optional Haystack filters to narrow down the search space.
+ :param top_k: Maximum number of documents to retrieve.
+ :param filter_policy: Policy to determine how runtime filters are combined with
+ initialization filters.
+ :raises ValueError: If the provided `document_store` is not a `FalkorDBDocumentStore`.
+ """
+ if not isinstance(document_store, FalkorDBDocumentStore):
+ msg = "document_store must be an instance of FalkorDBDocumentStore"
+ raise ValueError(msg)
+
+ self.document_store = document_store
+ self.filters = filters
+ self.top_k = top_k
+ self.filter_policy = FilterPolicy(filter_policy) if isinstance(filter_policy, str) else filter_policy
+
+ def to_dict(self) -> dict[str, Any]:
+ """Serialize this retriever to a dictionary."""
+ return default_to_dict(
+ self,
+ document_store=self.document_store.to_dict(),
+ filters=self.filters,
+ top_k=self.top_k,
+ filter_policy=self.filter_policy.value,
+ )
+
+ @classmethod
+ def from_dict(cls, data: dict[str, Any]) -> "FalkorDBEmbeddingRetriever":
+ """Deserialize a retriever from a dictionary."""
+ return default_from_dict(cls, data)
+
+ @component.output_types(documents=list[Document])
+ def run(
+ self,
+ query_embedding: list[float],
+ filters: dict[str, Any] | None = None,
+ top_k: int | None = None,
+ ) -> dict[str, list[Document]]:
+ """
+ Retrieve documents by vector similarity.
+
+ :param query_embedding: Query embedding vector.
+ :param filters: Optional Haystack filters to be combined with the init filters based
+ on the configured filter policy.
+ :param top_k: Maximum number of documents to return. If not provided, the default
+ top_k from initialization is used.
+ :returns: Dictionary containing a `"documents"` key with the retrieved documents.
+ """
+ final_filters = apply_filter_policy(self.filter_policy, self.filters, filters)
+ final_top_k = top_k if top_k is not None else self.top_k
+
+ docs = self.document_store._embedding_retrieval(
+ query_embedding=query_embedding,
+ top_k=final_top_k,
+ filters=final_filters,
+ )
+
+ return {"documents": docs}
diff --git a/integrations/falkordb/src/haystack_integrations/components/retrievers/falkordb/py.typed b/integrations/falkordb/src/haystack_integrations/components/retrievers/falkordb/py.typed
new file mode 100644
index 0000000000..8d1c8b69c3
--- /dev/null
+++ b/integrations/falkordb/src/haystack_integrations/components/retrievers/falkordb/py.typed
@@ -0,0 +1 @@
+
diff --git a/integrations/falkordb/src/haystack_integrations/document_stores/falkordb/Untitled-1.txt b/integrations/falkordb/src/haystack_integrations/document_stores/falkordb/Untitled-1.txt
new file mode 100644
index 0000000000..403b2e23db
--- /dev/null
+++ b/integrations/falkordb/src/haystack_integrations/document_stores/falkordb/Untitled-1.txt
@@ -0,0 +1,367 @@
+[Skip to main content](https://docs.haystack.deepset.ai/docs/metadata-filtering#__docusaurus_skipToContent_fallback)
+[Haystack Documentation](https://docs.haystack.deepset.ai/)
+[2.27](https://docs.haystack.deepset.ai/docs/metadata-filtering)
+- [2.28-unstable](https://docs.haystack.deepset.ai/docs/next/metadata-filtering)
+- [2.27](https://docs.haystack.deepset.ai/docs/metadata-filtering)
+- [2.26](https://docs.haystack.deepset.ai/docs/2.26/metadata-filtering)
+- [2.25](https://docs.haystack.deepset.ai/docs/2.25/metadata-filtering)
+- [2.24](https://docs.haystack.deepset.ai/docs/2.24/metadata-filtering)
+- [2.23](https://docs.haystack.deepset.ai/docs/2.23/metadata-filtering)
+-
+- [1.x archived documentation](https://docs.haystack.deepset.ai/docs/faq#where-can-i-find-tutorials-and-documentation-for-haystack-1x)
+- [2.x archived documentation](https://docs.haystack.deepset.ai/docs/faq#where-can-i-find-documentation-for-older-haystack-versions)
+[2.28-unstable](https://docs.haystack.deepset.ai/docs/next/metadata-filtering)
+[2.27](https://docs.haystack.deepset.ai/docs/metadata-filtering)
+[2.26](https://docs.haystack.deepset.ai/docs/2.26/metadata-filtering)
+[2.25](https://docs.haystack.deepset.ai/docs/2.25/metadata-filtering)
+[2.24](https://docs.haystack.deepset.ai/docs/2.24/metadata-filtering)
+[2.23](https://docs.haystack.deepset.ai/docs/2.23/metadata-filtering)
+[1.x archived documentation](https://docs.haystack.deepset.ai/docs/faq#where-can-i-find-tutorials-and-documentation-for-haystack-1x)
+[2.x archived documentation](https://docs.haystack.deepset.ai/docs/faq#where-can-i-find-documentation-for-older-haystack-versions)
+[Docs](https://docs.haystack.deepset.ai/docs/intro)
+[API Reference](https://docs.haystack.deepset.ai/reference/)
+[Contribute](https://github.com/deepset-ai/haystack/blob/main/docs-website/CONTRIBUTING.md)
+[GitHub](https://github.com/deepset-ai/haystack/tree/main/docs-website)
+- [Introduction](https://docs.haystack.deepset.ai/docs/intro)
+- [Overview](https://docs.haystack.deepset.ai/docs/installation)
+- [Haystack Concepts](https://docs.haystack.deepset.ai/docs/concepts-overview)[Haystack Concepts Overview](https://docs.haystack.deepset.ai/docs/concepts-overview)[Agents](https://docs.haystack.deepset.ai/docs/agents)[Components](https://docs.haystack.deepset.ai/docs/components)[Pipelines](https://docs.haystack.deepset.ai/docs/pipelines)[Data Classes](https://docs.haystack.deepset.ai/docs/data-classes)[Document Store](https://docs.haystack.deepset.ai/docs/document-store)[Metadata Filtering](https://docs.haystack.deepset.ai/docs/metadata-filtering)[Device Management](https://docs.haystack.deepset.ai/docs/device-management)[Secret Management](https://docs.haystack.deepset.ai/docs/secret-management)[Jinja Templates](https://docs.haystack.deepset.ai/docs/jinja-templates)[Introduction to Integrations](https://docs.haystack.deepset.ai/docs/integrations)[Experimental Package](https://docs.haystack.deepset.ai/docs/experimental-package)
+- [Haystack Concepts Overview](https://docs.haystack.deepset.ai/docs/concepts-overview)
+- [Agents](https://docs.haystack.deepset.ai/docs/agents)
+- [Components](https://docs.haystack.deepset.ai/docs/components)
+- [Pipelines](https://docs.haystack.deepset.ai/docs/pipelines)
+- [Data Classes](https://docs.haystack.deepset.ai/docs/data-classes)
+- [Document Store](https://docs.haystack.deepset.ai/docs/document-store)
+- [Metadata Filtering](https://docs.haystack.deepset.ai/docs/metadata-filtering)
+- [Device Management](https://docs.haystack.deepset.ai/docs/device-management)
+- [Secret Management](https://docs.haystack.deepset.ai/docs/secret-management)
+- [Jinja Templates](https://docs.haystack.deepset.ai/docs/jinja-templates)
+- [Introduction to Integrations](https://docs.haystack.deepset.ai/docs/integrations)
+- [Experimental Package](https://docs.haystack.deepset.ai/docs/experimental-package)
+- [Document Stores](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore)
+- [Pipeline Components](https://docs.haystack.deepset.ai/docs/agent)
+- [Tools](https://docs.haystack.deepset.ai/docs/tool)
+- [Optimization](https://docs.haystack.deepset.ai/docs/evaluation)
+- [Development](https://docs.haystack.deepset.ai/docs/logging)
+[Introduction](https://docs.haystack.deepset.ai/docs/intro)
+[Overview](https://docs.haystack.deepset.ai/docs/installation)
+[Haystack Concepts](https://docs.haystack.deepset.ai/docs/concepts-overview)
+
+- [Haystack Concepts Overview](https://docs.haystack.deepset.ai/docs/concepts-overview)
+- [Agents](https://docs.haystack.deepset.ai/docs/agents)
+- [Components](https://docs.haystack.deepset.ai/docs/components)
+- [Pipelines](https://docs.haystack.deepset.ai/docs/pipelines)
+- [Data Classes](https://docs.haystack.deepset.ai/docs/data-classes)
+- [Document Store](https://docs.haystack.deepset.ai/docs/document-store)
+- [Metadata Filtering](https://docs.haystack.deepset.ai/docs/metadata-filtering)
+- [Device Management](https://docs.haystack.deepset.ai/docs/device-management)
+- [Secret Management](https://docs.haystack.deepset.ai/docs/secret-management)
+- [Jinja Templates](https://docs.haystack.deepset.ai/docs/jinja-templates)
+- [Introduction to Integrations](https://docs.haystack.deepset.ai/docs/integrations)
+- [Experimental Package](https://docs.haystack.deepset.ai/docs/experimental-package)
+[Haystack Concepts Overview](https://docs.haystack.deepset.ai/docs/concepts-overview)
+[Agents](https://docs.haystack.deepset.ai/docs/agents)
+[Components](https://docs.haystack.deepset.ai/docs/components)
+[Pipelines](https://docs.haystack.deepset.ai/docs/pipelines)
+[Data Classes](https://docs.haystack.deepset.ai/docs/data-classes)
+[Document Store](https://docs.haystack.deepset.ai/docs/document-store)
+[Metadata Filtering](https://docs.haystack.deepset.ai/docs/metadata-filtering)
+[Device Management](https://docs.haystack.deepset.ai/docs/device-management)
+[Secret Management](https://docs.haystack.deepset.ai/docs/secret-management)
+[Jinja Templates](https://docs.haystack.deepset.ai/docs/jinja-templates)
+[Introduction to Integrations](https://docs.haystack.deepset.ai/docs/integrations)
+[Experimental Package](https://docs.haystack.deepset.ai/docs/experimental-package)
+[Document Stores](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore)
+[Pipeline Components](https://docs.haystack.deepset.ai/docs/agent)
+[Tools](https://docs.haystack.deepset.ai/docs/tool)
+[Optimization](https://docs.haystack.deepset.ai/docs/evaluation)
+[Development](https://docs.haystack.deepset.ai/docs/logging)
+- https://docs.haystack.deepset.ai/
+- Haystack Concepts
+- Metadata Filtering
+
+# Metadata Filtering
+
+This page provides a detailed explanation of how to apply metadata filters at query time.
+When you index documents into your Document Store, you can attach metadata to them. One example is the DocumentLanguageClassifier, which adds the language of the document's content to its metadata. Components like MetadataRouter can then route documents based on their metadata.
+
+```
+DocumentLanguageClassifier
+```
+
+
+```
+MetadataRouter
+```
+
+You can then use the metadata to filter your search queries, allowing you to narrow down the results by focusing on specific criteria. This ensures your Retriever fetches answers from the most relevant subset of your data.
+To illustrate how metadata filters work, imagine you have a set of annual reports from various companies. You may want to perform a search on just a specific year and just on a small selection of companies. This can reduce the workload of the Retriever and also ensure that you get more relevant results.
+
+## Filtering Types[](https://docs.haystack.deepset.ai/docs/metadata-filtering#filtering-types)
+
+[](https://docs.haystack.deepset.ai/docs/metadata-filtering#filtering-types)
+Filters are defined as a dictionary or nested dictionaries that can be of two types: Comparison or Logic.
+
+### Comparison[](https://docs.haystack.deepset.ai/docs/metadata-filtering#comparison)
+[](https://docs.haystack.deepset.ai/docs/metadata-filtering#comparison)
+Comparison operators help search your metadata fields according the specified conditions.
+Comparison dictionaries must contain the following keys:
+-field: the name of one of the meta fields of a document, such as meta.years.
+
+```
+field
+```
+
+
+```
+meta.years
+```
+
+-operator: must be one of the following:
+
+```
+operator
+```
+
+
+```
+- `==` - `!=` - `>` - `>=` - `<` - `<=` - `in` - `not in`
+```
+
+
+```
+- `==` - `!=` - `>` - `>=` - `<` - `<=` - `in` - `not in`
+```
+
+The available comparison operators may vary depending on the specific Document Store integration. For example, the ChromaDocumentStore supports two additional operators: contains and not contains. Find the details about the supported filters in the specific integration’s API reference.
+
+```
+ChromaDocumentStore
+```
+
+
+```
+contains
+```
+
+
+```
+not contains
+```
+
+-value: takes a single value or (in the case of "in" and “not in”) a list of values.
+
+```
+value
+```
+
+
+#### Example[](https://docs.haystack.deepset.ai/docs/metadata-filtering#example)
+[](https://docs.haystack.deepset.ai/docs/metadata-filtering#example)
+Here is an example of a simple filter in the form of a dictionary. The filter selects documents classified as “article” in the type meta field of the document:
+
+```
+type
+```
+
+
+```
+filters = {"field": "meta.type", "operator": "==", "value": "article"}
+```
+
+
+```
+filters = {"field": "meta.type", "operator": "==", "value": "article"}
+```
+
+### Logic[](https://docs.haystack.deepset.ai/docs/metadata-filtering#logic)
+[](https://docs.haystack.deepset.ai/docs/metadata-filtering#logic)
+Logical operators can be used to create a nested dictionary, allowing you to apply multiple fields as filter conditions. Logic dictionaries must contain the following keys:
+
+```
+fields
+```
+
+-operator: usually one of the following:
+
+```
+operator
+```
+
+
+```
+- `NOT` - `OR` - `AND`
+```
+
+
+```
+- `NOT` - `OR` - `AND`
+```
+
+The available logic operators may vary depending on the specific Document Store integration. For example, the ChromaDocumentStore doesn’t support the NOT operator. Find the details about the supported filters in the specific integration’s API reference.
+
+```
+ChromaDocumentStore
+```
+
+
+```
+NOT
+```
+
+-conditions: must be a list of dictionaries, either of type Comparison or Logic.
+
+```
+conditions
+```
+
+
+#### Nested Filter Example[](https://docs.haystack.deepset.ai/docs/metadata-filtering#nested-filter-example)
+[](https://docs.haystack.deepset.ai/docs/metadata-filtering#nested-filter-example)
+Here is a more complex filter that uses both Comparison and Logic to find documents where:
+- Meta field type is "article",
+- Meta field date is between 1420066800 and 1609455600 (a specific date range),
+- Meta field rating is greater than or equal to 3,
+- Documents are either classified as genre ["economy", "politics"] OR the meta field publisher is "nytimes".
+
+```
+type
+```
+
+
+```
+date
+```
+
+
+```
+rating
+```
+
+
+```
+genre
+```
+
+
+```
+OR
+```
+
+
+```
+publisher
+```
+
+
+```
+filters = { "operator": "AND", "conditions": [ {"field": "meta.type", "operator": "==", "value": "article"}, {"field": "meta.date", "operator": ">=", "value": 1420066800}, {"field": "meta.date", "operator": "<", "value": 1609455600}, {"field": "meta.rating", "operator": ">=", "value": 3}, { "operator": "OR", "conditions": [ { "field": "meta.genre", "operator": "in", "value": ["economy", "politics"], }, {"field": "meta.publisher", "operator": "==", "value": "nytimes"}, ], }, ],}
+```
+
+
+```
+filters = { "operator": "AND", "conditions": [ {"field": "meta.type", "operator": "==", "value": "article"}, {"field": "meta.date", "operator": ">=", "value": 1420066800}, {"field": "meta.date", "operator": "<", "value": 1609455600}, {"field": "meta.rating", "operator": ">=", "value": 3}, { "operator": "OR", "conditions": [ { "field": "meta.genre", "operator": "in", "value": ["economy", "politics"], }, {"field": "meta.publisher", "operator": "==", "value": "nytimes"}, ], }, ],}
+```
+
+## Filters Usage[](https://docs.haystack.deepset.ai/docs/metadata-filtering#filters-usage)
+
+[](https://docs.haystack.deepset.ai/docs/metadata-filtering#filters-usage)
+Filters can be applied either through the Retriever class or directly within Document Stores.
+
+```
+Retriever
+```
+
+In the Retriever class, filters are passed through the filters argument. When working with a pipeline, filters can be provided to Pipeline.run(), which will automatically route them to the Retriever class (refer to the [pipelines documentation](https://docs.haystack.deepset.ai/docs/pipelines) for more information on working with pipelines).
+
+```
+Retriever
+```
+
+
+```
+filters
+```
+
+
+```
+Pipeline.run()
+```
+
+
+```
+Retriever
+```
+
+The example below shows how filters can be passed to Retrievers within a pipeline:
+
+```
+pipeline.run( data={ "retriever": { "query": "Why did the revenue increase?", "filters": { "operator": "AND", "conditions": [ {"field": "meta.years", "operator": "==", "value": "2019"}, { "field": "meta.companies", "operator": "in", "value": ["BMW", "Mercedes"], }, ], }, }, },)
+```
+
+
+```
+pipeline.run( data={ "retriever": { "query": "Why did the revenue increase?", "filters": { "operator": "AND", "conditions": [ {"field": "meta.years", "operator": "==", "value": "2019"}, { "field": "meta.companies", "operator": "in", "value": ["BMW", "Mercedes"], }, ], }, }, },)
+```
+
+In Document Stores, the filter_documents method is used to apply filters to stored documents, if the specific integration supports filtering.
+
+```
+filter_documents
+```
+
+The example below shows how filters can be passed to the QdrantDocumentStore:
+
+```
+QdrantDocumentStore
+```
+
+
+```
+filters = { "operator": "AND", "conditions": [ {"field": "meta.type", "operator": "==", "value": "article"}, {"field": "meta.genre", "operator": "in", "value": ["economy", "politics"]}, ],}results = QdrantDocumentStore.filter_documents(filters=filters)
+```
+
+
+```
+filters = { "operator": "AND", "conditions": [ {"field": "meta.type", "operator": "==", "value": "article"}, {"field": "meta.genre", "operator": "in", "value": ["economy", "politics"]}, ],}results = QdrantDocumentStore.filter_documents(filters=filters)
+```
+
+## Additional References[](https://docs.haystack.deepset.ai/docs/metadata-filtering#additional-references)
+
+[](https://docs.haystack.deepset.ai/docs/metadata-filtering#additional-references)
+📓 Tutorial: [Filtering Documents with Metadata](https://haystack.deepset.ai/tutorials/31_metadata_filtering)
+🧑🍳 Cookbook: [Extracting Metadata Filters from a Query](https://haystack.deepset.ai/cookbook/extracting_metadata_filters_from_a_user_query)
+[Edit this page](https://github.com/deepset-ai/haystack/tree/main/docs-website/versioned_docs/version-2.27/concepts/metadata-filtering.mdx)
+[PreviousCreating Custom Document Stores](https://docs.haystack.deepset.ai/docs/creating-custom-document-stores)
+[NextDevice Management](https://docs.haystack.deepset.ai/docs/device-management)
+- [Filtering Types](https://docs.haystack.deepset.ai/docs/metadata-filtering#filtering-types)[Comparison](https://docs.haystack.deepset.ai/docs/metadata-filtering#comparison)[Logic](https://docs.haystack.deepset.ai/docs/metadata-filtering#logic)
+- [Comparison](https://docs.haystack.deepset.ai/docs/metadata-filtering#comparison)
+- [Logic](https://docs.haystack.deepset.ai/docs/metadata-filtering#logic)
+- [Filters Usage](https://docs.haystack.deepset.ai/docs/metadata-filtering#filters-usage)
+- [Additional References](https://docs.haystack.deepset.ai/docs/metadata-filtering#additional-references)
+[Filtering Types](https://docs.haystack.deepset.ai/docs/metadata-filtering#filtering-types)
+- [Comparison](https://docs.haystack.deepset.ai/docs/metadata-filtering#comparison)
+- [Logic](https://docs.haystack.deepset.ai/docs/metadata-filtering#logic)
+[Comparison](https://docs.haystack.deepset.ai/docs/metadata-filtering#comparison)
+[Logic](https://docs.haystack.deepset.ai/docs/metadata-filtering#logic)
+[Filters Usage](https://docs.haystack.deepset.ai/docs/metadata-filtering#filters-usage)
+[Additional References](https://docs.haystack.deepset.ai/docs/metadata-filtering#additional-references)
+- [Tutorials](https://haystack.deepset.ai/tutorials)
+- [Cookbooks](https://haystack.deepset.ai/cookbook)
+[Tutorials](https://haystack.deepset.ai/tutorials)
+[Cookbooks](https://haystack.deepset.ai/cookbook)
+- [Integrations](https://haystack.deepset.ai/integrations)
+- [Platform - Try Free](https://landing.deepset.ai/deepset-studio-signup)
+- [Enterprise Support](https://landing.deepset.ai/deepset-studio-signup)
+[Integrations](https://haystack.deepset.ai/integrations)
+[Platform - Try Free](https://landing.deepset.ai/deepset-studio-signup)
+[Enterprise Support](https://landing.deepset.ai/deepset-studio-signup)
+- [About](https://deepset.ai/about)
+- [Careers](https://deepset.ai/careers)
+- [Blog](https://deepset.ai/blog)
+[About](https://deepset.ai/about)
+[Careers](https://deepset.ai/careers)
+[Blog](https://deepset.ai/blog)
+- [Privacy Policy](https://www.deepset.ai/privacy-policy)
+- [Imprint](https://www.deepset.ai/imprint)
+[Privacy Policy](https://www.deepset.ai/privacy-policy)
+[Imprint](https://www.deepset.ai/imprint)
\ No newline at end of file
diff --git a/integrations/falkordb/src/haystack_integrations/document_stores/falkordb/Untitled-2.txt b/integrations/falkordb/src/haystack_integrations/document_stores/falkordb/Untitled-2.txt
new file mode 100644
index 0000000000..403b2e23db
--- /dev/null
+++ b/integrations/falkordb/src/haystack_integrations/document_stores/falkordb/Untitled-2.txt
@@ -0,0 +1,367 @@
+[Skip to main content](https://docs.haystack.deepset.ai/docs/metadata-filtering#__docusaurus_skipToContent_fallback)
+[Haystack Documentation](https://docs.haystack.deepset.ai/)
+[2.27](https://docs.haystack.deepset.ai/docs/metadata-filtering)
+- [2.28-unstable](https://docs.haystack.deepset.ai/docs/next/metadata-filtering)
+- [2.27](https://docs.haystack.deepset.ai/docs/metadata-filtering)
+- [2.26](https://docs.haystack.deepset.ai/docs/2.26/metadata-filtering)
+- [2.25](https://docs.haystack.deepset.ai/docs/2.25/metadata-filtering)
+- [2.24](https://docs.haystack.deepset.ai/docs/2.24/metadata-filtering)
+- [2.23](https://docs.haystack.deepset.ai/docs/2.23/metadata-filtering)
+-
+- [1.x archived documentation](https://docs.haystack.deepset.ai/docs/faq#where-can-i-find-tutorials-and-documentation-for-haystack-1x)
+- [2.x archived documentation](https://docs.haystack.deepset.ai/docs/faq#where-can-i-find-documentation-for-older-haystack-versions)
+[2.28-unstable](https://docs.haystack.deepset.ai/docs/next/metadata-filtering)
+[2.27](https://docs.haystack.deepset.ai/docs/metadata-filtering)
+[2.26](https://docs.haystack.deepset.ai/docs/2.26/metadata-filtering)
+[2.25](https://docs.haystack.deepset.ai/docs/2.25/metadata-filtering)
+[2.24](https://docs.haystack.deepset.ai/docs/2.24/metadata-filtering)
+[2.23](https://docs.haystack.deepset.ai/docs/2.23/metadata-filtering)
+[1.x archived documentation](https://docs.haystack.deepset.ai/docs/faq#where-can-i-find-tutorials-and-documentation-for-haystack-1x)
+[2.x archived documentation](https://docs.haystack.deepset.ai/docs/faq#where-can-i-find-documentation-for-older-haystack-versions)
+[Docs](https://docs.haystack.deepset.ai/docs/intro)
+[API Reference](https://docs.haystack.deepset.ai/reference/)
+[Contribute](https://github.com/deepset-ai/haystack/blob/main/docs-website/CONTRIBUTING.md)
+[GitHub](https://github.com/deepset-ai/haystack/tree/main/docs-website)
+- [Introduction](https://docs.haystack.deepset.ai/docs/intro)
+- [Overview](https://docs.haystack.deepset.ai/docs/installation)
+- [Haystack Concepts](https://docs.haystack.deepset.ai/docs/concepts-overview)[Haystack Concepts Overview](https://docs.haystack.deepset.ai/docs/concepts-overview)[Agents](https://docs.haystack.deepset.ai/docs/agents)[Components](https://docs.haystack.deepset.ai/docs/components)[Pipelines](https://docs.haystack.deepset.ai/docs/pipelines)[Data Classes](https://docs.haystack.deepset.ai/docs/data-classes)[Document Store](https://docs.haystack.deepset.ai/docs/document-store)[Metadata Filtering](https://docs.haystack.deepset.ai/docs/metadata-filtering)[Device Management](https://docs.haystack.deepset.ai/docs/device-management)[Secret Management](https://docs.haystack.deepset.ai/docs/secret-management)[Jinja Templates](https://docs.haystack.deepset.ai/docs/jinja-templates)[Introduction to Integrations](https://docs.haystack.deepset.ai/docs/integrations)[Experimental Package](https://docs.haystack.deepset.ai/docs/experimental-package)
+- [Haystack Concepts Overview](https://docs.haystack.deepset.ai/docs/concepts-overview)
+- [Agents](https://docs.haystack.deepset.ai/docs/agents)
+- [Components](https://docs.haystack.deepset.ai/docs/components)
+- [Pipelines](https://docs.haystack.deepset.ai/docs/pipelines)
+- [Data Classes](https://docs.haystack.deepset.ai/docs/data-classes)
+- [Document Store](https://docs.haystack.deepset.ai/docs/document-store)
+- [Metadata Filtering](https://docs.haystack.deepset.ai/docs/metadata-filtering)
+- [Device Management](https://docs.haystack.deepset.ai/docs/device-management)
+- [Secret Management](https://docs.haystack.deepset.ai/docs/secret-management)
+- [Jinja Templates](https://docs.haystack.deepset.ai/docs/jinja-templates)
+- [Introduction to Integrations](https://docs.haystack.deepset.ai/docs/integrations)
+- [Experimental Package](https://docs.haystack.deepset.ai/docs/experimental-package)
+- [Document Stores](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore)
+- [Pipeline Components](https://docs.haystack.deepset.ai/docs/agent)
+- [Tools](https://docs.haystack.deepset.ai/docs/tool)
+- [Optimization](https://docs.haystack.deepset.ai/docs/evaluation)
+- [Development](https://docs.haystack.deepset.ai/docs/logging)
+[Introduction](https://docs.haystack.deepset.ai/docs/intro)
+[Overview](https://docs.haystack.deepset.ai/docs/installation)
+[Haystack Concepts](https://docs.haystack.deepset.ai/docs/concepts-overview)
+
+- [Haystack Concepts Overview](https://docs.haystack.deepset.ai/docs/concepts-overview)
+- [Agents](https://docs.haystack.deepset.ai/docs/agents)
+- [Components](https://docs.haystack.deepset.ai/docs/components)
+- [Pipelines](https://docs.haystack.deepset.ai/docs/pipelines)
+- [Data Classes](https://docs.haystack.deepset.ai/docs/data-classes)
+- [Document Store](https://docs.haystack.deepset.ai/docs/document-store)
+- [Metadata Filtering](https://docs.haystack.deepset.ai/docs/metadata-filtering)
+- [Device Management](https://docs.haystack.deepset.ai/docs/device-management)
+- [Secret Management](https://docs.haystack.deepset.ai/docs/secret-management)
+- [Jinja Templates](https://docs.haystack.deepset.ai/docs/jinja-templates)
+- [Introduction to Integrations](https://docs.haystack.deepset.ai/docs/integrations)
+- [Experimental Package](https://docs.haystack.deepset.ai/docs/experimental-package)
+[Haystack Concepts Overview](https://docs.haystack.deepset.ai/docs/concepts-overview)
+[Agents](https://docs.haystack.deepset.ai/docs/agents)
+[Components](https://docs.haystack.deepset.ai/docs/components)
+[Pipelines](https://docs.haystack.deepset.ai/docs/pipelines)
+[Data Classes](https://docs.haystack.deepset.ai/docs/data-classes)
+[Document Store](https://docs.haystack.deepset.ai/docs/document-store)
+[Metadata Filtering](https://docs.haystack.deepset.ai/docs/metadata-filtering)
+[Device Management](https://docs.haystack.deepset.ai/docs/device-management)
+[Secret Management](https://docs.haystack.deepset.ai/docs/secret-management)
+[Jinja Templates](https://docs.haystack.deepset.ai/docs/jinja-templates)
+[Introduction to Integrations](https://docs.haystack.deepset.ai/docs/integrations)
+[Experimental Package](https://docs.haystack.deepset.ai/docs/experimental-package)
+[Document Stores](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore)
+[Pipeline Components](https://docs.haystack.deepset.ai/docs/agent)
+[Tools](https://docs.haystack.deepset.ai/docs/tool)
+[Optimization](https://docs.haystack.deepset.ai/docs/evaluation)
+[Development](https://docs.haystack.deepset.ai/docs/logging)
+- https://docs.haystack.deepset.ai/
+- Haystack Concepts
+- Metadata Filtering
+
+# Metadata Filtering
+
+This page provides a detailed explanation of how to apply metadata filters at query time.
+When you index documents into your Document Store, you can attach metadata to them. One example is the DocumentLanguageClassifier, which adds the language of the document's content to its metadata. Components like MetadataRouter can then route documents based on their metadata.
+
+```
+DocumentLanguageClassifier
+```
+
+
+```
+MetadataRouter
+```
+
+You can then use the metadata to filter your search queries, allowing you to narrow down the results by focusing on specific criteria. This ensures your Retriever fetches answers from the most relevant subset of your data.
+To illustrate how metadata filters work, imagine you have a set of annual reports from various companies. You may want to perform a search on just a specific year and just on a small selection of companies. This can reduce the workload of the Retriever and also ensure that you get more relevant results.
+
+## Filtering Types[](https://docs.haystack.deepset.ai/docs/metadata-filtering#filtering-types)
+
+[](https://docs.haystack.deepset.ai/docs/metadata-filtering#filtering-types)
+Filters are defined as a dictionary or nested dictionaries that can be of two types: Comparison or Logic.
+
+### Comparison[](https://docs.haystack.deepset.ai/docs/metadata-filtering#comparison)
+[](https://docs.haystack.deepset.ai/docs/metadata-filtering#comparison)
+Comparison operators help search your metadata fields according the specified conditions.
+Comparison dictionaries must contain the following keys:
+-field: the name of one of the meta fields of a document, such as meta.years.
+
+```
+field
+```
+
+
+```
+meta.years
+```
+
+-operator: must be one of the following:
+
+```
+operator
+```
+
+
+```
+- `==` - `!=` - `>` - `>=` - `<` - `<=` - `in` - `not in`
+```
+
+
+```
+- `==` - `!=` - `>` - `>=` - `<` - `<=` - `in` - `not in`
+```
+
+The available comparison operators may vary depending on the specific Document Store integration. For example, the ChromaDocumentStore supports two additional operators: contains and not contains. Find the details about the supported filters in the specific integration’s API reference.
+
+```
+ChromaDocumentStore
+```
+
+
+```
+contains
+```
+
+
+```
+not contains
+```
+
+-value: takes a single value or (in the case of "in" and “not in”) a list of values.
+
+```
+value
+```
+
+
+#### Example[](https://docs.haystack.deepset.ai/docs/metadata-filtering#example)
+[](https://docs.haystack.deepset.ai/docs/metadata-filtering#example)
+Here is an example of a simple filter in the form of a dictionary. The filter selects documents classified as “article” in the type meta field of the document:
+
+```
+type
+```
+
+
+```
+filters = {"field": "meta.type", "operator": "==", "value": "article"}
+```
+
+
+```
+filters = {"field": "meta.type", "operator": "==", "value": "article"}
+```
+
+### Logic[](https://docs.haystack.deepset.ai/docs/metadata-filtering#logic)
+[](https://docs.haystack.deepset.ai/docs/metadata-filtering#logic)
+Logical operators can be used to create a nested dictionary, allowing you to apply multiple fields as filter conditions. Logic dictionaries must contain the following keys:
+
+```
+fields
+```
+
+-operator: usually one of the following:
+
+```
+operator
+```
+
+
+```
+- `NOT` - `OR` - `AND`
+```
+
+
+```
+- `NOT` - `OR` - `AND`
+```
+
+The available logic operators may vary depending on the specific Document Store integration. For example, the ChromaDocumentStore doesn’t support the NOT operator. Find the details about the supported filters in the specific integration’s API reference.
+
+```
+ChromaDocumentStore
+```
+
+
+```
+NOT
+```
+
+-conditions: must be a list of dictionaries, either of type Comparison or Logic.
+
+```
+conditions
+```
+
+
+#### Nested Filter Example[](https://docs.haystack.deepset.ai/docs/metadata-filtering#nested-filter-example)
+[](https://docs.haystack.deepset.ai/docs/metadata-filtering#nested-filter-example)
+Here is a more complex filter that uses both Comparison and Logic to find documents where:
+- Meta field type is "article",
+- Meta field date is between 1420066800 and 1609455600 (a specific date range),
+- Meta field rating is greater than or equal to 3,
+- Documents are either classified as genre ["economy", "politics"] OR the meta field publisher is "nytimes".
+
+```
+type
+```
+
+
+```
+date
+```
+
+
+```
+rating
+```
+
+
+```
+genre
+```
+
+
+```
+OR
+```
+
+
+```
+publisher
+```
+
+
+```
+filters = { "operator": "AND", "conditions": [ {"field": "meta.type", "operator": "==", "value": "article"}, {"field": "meta.date", "operator": ">=", "value": 1420066800}, {"field": "meta.date", "operator": "<", "value": 1609455600}, {"field": "meta.rating", "operator": ">=", "value": 3}, { "operator": "OR", "conditions": [ { "field": "meta.genre", "operator": "in", "value": ["economy", "politics"], }, {"field": "meta.publisher", "operator": "==", "value": "nytimes"}, ], }, ],}
+```
+
+
+```
+filters = { "operator": "AND", "conditions": [ {"field": "meta.type", "operator": "==", "value": "article"}, {"field": "meta.date", "operator": ">=", "value": 1420066800}, {"field": "meta.date", "operator": "<", "value": 1609455600}, {"field": "meta.rating", "operator": ">=", "value": 3}, { "operator": "OR", "conditions": [ { "field": "meta.genre", "operator": "in", "value": ["economy", "politics"], }, {"field": "meta.publisher", "operator": "==", "value": "nytimes"}, ], }, ],}
+```
+
+## Filters Usage[](https://docs.haystack.deepset.ai/docs/metadata-filtering#filters-usage)
+
+[](https://docs.haystack.deepset.ai/docs/metadata-filtering#filters-usage)
+Filters can be applied either through the Retriever class or directly within Document Stores.
+
+```
+Retriever
+```
+
+In the Retriever class, filters are passed through the filters argument. When working with a pipeline, filters can be provided to Pipeline.run(), which will automatically route them to the Retriever class (refer to the [pipelines documentation](https://docs.haystack.deepset.ai/docs/pipelines) for more information on working with pipelines).
+
+```
+Retriever
+```
+
+
+```
+filters
+```
+
+
+```
+Pipeline.run()
+```
+
+
+```
+Retriever
+```
+
+The example below shows how filters can be passed to Retrievers within a pipeline:
+
+```
+pipeline.run( data={ "retriever": { "query": "Why did the revenue increase?", "filters": { "operator": "AND", "conditions": [ {"field": "meta.years", "operator": "==", "value": "2019"}, { "field": "meta.companies", "operator": "in", "value": ["BMW", "Mercedes"], }, ], }, }, },)
+```
+
+
+```
+pipeline.run( data={ "retriever": { "query": "Why did the revenue increase?", "filters": { "operator": "AND", "conditions": [ {"field": "meta.years", "operator": "==", "value": "2019"}, { "field": "meta.companies", "operator": "in", "value": ["BMW", "Mercedes"], }, ], }, }, },)
+```
+
+In Document Stores, the filter_documents method is used to apply filters to stored documents, if the specific integration supports filtering.
+
+```
+filter_documents
+```
+
+The example below shows how filters can be passed to the QdrantDocumentStore:
+
+```
+QdrantDocumentStore
+```
+
+
+```
+filters = { "operator": "AND", "conditions": [ {"field": "meta.type", "operator": "==", "value": "article"}, {"field": "meta.genre", "operator": "in", "value": ["economy", "politics"]}, ],}results = QdrantDocumentStore.filter_documents(filters=filters)
+```
+
+
+```
+filters = { "operator": "AND", "conditions": [ {"field": "meta.type", "operator": "==", "value": "article"}, {"field": "meta.genre", "operator": "in", "value": ["economy", "politics"]}, ],}results = QdrantDocumentStore.filter_documents(filters=filters)
+```
+
+## Additional References[](https://docs.haystack.deepset.ai/docs/metadata-filtering#additional-references)
+
+[](https://docs.haystack.deepset.ai/docs/metadata-filtering#additional-references)
+📓 Tutorial: [Filtering Documents with Metadata](https://haystack.deepset.ai/tutorials/31_metadata_filtering)
+🧑🍳 Cookbook: [Extracting Metadata Filters from a Query](https://haystack.deepset.ai/cookbook/extracting_metadata_filters_from_a_user_query)
+[Edit this page](https://github.com/deepset-ai/haystack/tree/main/docs-website/versioned_docs/version-2.27/concepts/metadata-filtering.mdx)
+[PreviousCreating Custom Document Stores](https://docs.haystack.deepset.ai/docs/creating-custom-document-stores)
+[NextDevice Management](https://docs.haystack.deepset.ai/docs/device-management)
+- [Filtering Types](https://docs.haystack.deepset.ai/docs/metadata-filtering#filtering-types)[Comparison](https://docs.haystack.deepset.ai/docs/metadata-filtering#comparison)[Logic](https://docs.haystack.deepset.ai/docs/metadata-filtering#logic)
+- [Comparison](https://docs.haystack.deepset.ai/docs/metadata-filtering#comparison)
+- [Logic](https://docs.haystack.deepset.ai/docs/metadata-filtering#logic)
+- [Filters Usage](https://docs.haystack.deepset.ai/docs/metadata-filtering#filters-usage)
+- [Additional References](https://docs.haystack.deepset.ai/docs/metadata-filtering#additional-references)
+[Filtering Types](https://docs.haystack.deepset.ai/docs/metadata-filtering#filtering-types)
+- [Comparison](https://docs.haystack.deepset.ai/docs/metadata-filtering#comparison)
+- [Logic](https://docs.haystack.deepset.ai/docs/metadata-filtering#logic)
+[Comparison](https://docs.haystack.deepset.ai/docs/metadata-filtering#comparison)
+[Logic](https://docs.haystack.deepset.ai/docs/metadata-filtering#logic)
+[Filters Usage](https://docs.haystack.deepset.ai/docs/metadata-filtering#filters-usage)
+[Additional References](https://docs.haystack.deepset.ai/docs/metadata-filtering#additional-references)
+- [Tutorials](https://haystack.deepset.ai/tutorials)
+- [Cookbooks](https://haystack.deepset.ai/cookbook)
+[Tutorials](https://haystack.deepset.ai/tutorials)
+[Cookbooks](https://haystack.deepset.ai/cookbook)
+- [Integrations](https://haystack.deepset.ai/integrations)
+- [Platform - Try Free](https://landing.deepset.ai/deepset-studio-signup)
+- [Enterprise Support](https://landing.deepset.ai/deepset-studio-signup)
+[Integrations](https://haystack.deepset.ai/integrations)
+[Platform - Try Free](https://landing.deepset.ai/deepset-studio-signup)
+[Enterprise Support](https://landing.deepset.ai/deepset-studio-signup)
+- [About](https://deepset.ai/about)
+- [Careers](https://deepset.ai/careers)
+- [Blog](https://deepset.ai/blog)
+[About](https://deepset.ai/about)
+[Careers](https://deepset.ai/careers)
+[Blog](https://deepset.ai/blog)
+- [Privacy Policy](https://www.deepset.ai/privacy-policy)
+- [Imprint](https://www.deepset.ai/imprint)
+[Privacy Policy](https://www.deepset.ai/privacy-policy)
+[Imprint](https://www.deepset.ai/imprint)
\ No newline at end of file
diff --git a/integrations/falkordb/src/haystack_integrations/document_stores/falkordb/__init__.py b/integrations/falkordb/src/haystack_integrations/document_stores/falkordb/__init__.py
new file mode 100644
index 0000000000..e79e7e528a
--- /dev/null
+++ b/integrations/falkordb/src/haystack_integrations/document_stores/falkordb/__init__.py
@@ -0,0 +1,10 @@
+# SPDX-FileCopyrightText: 2024-present deepset GmbH
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from haystack_integrations.document_stores.falkordb.document_store import (
+ FalkorDBDocumentStore,
+ SimilarityFunction,
+)
+
+__all__ = ["FalkorDBDocumentStore", "SimilarityFunction"]
diff --git a/integrations/falkordb/src/haystack_integrations/document_stores/falkordb/document_store.py b/integrations/falkordb/src/haystack_integrations/document_stores/falkordb/document_store.py
new file mode 100644
index 0000000000..841832b31b
--- /dev/null
+++ b/integrations/falkordb/src/haystack_integrations/document_stores/falkordb/document_store.py
@@ -0,0 +1,702 @@
+# SPDX-FileCopyrightText: 2024-present deepset GmbH
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import logging
+import math
+from dataclasses import replace
+from datetime import datetime
+from typing import Any, Literal
+
+from haystack import default_from_dict, default_to_dict
+from haystack.dataclasses import Document
+from haystack.document_stores.errors import DocumentStoreError, DuplicateDocumentError
+from haystack.document_stores.types import DocumentStore, DuplicatePolicy
+from haystack.errors import FilterError
+from haystack.utils import Secret
+from redis.exceptions import ResponseError
+
+import falkordb # type: ignore[import-untyped,import-not-found]
+
+logger = logging.getLogger(__name__)
+
+# Haystack filter operators → Cypher comparison operators.
+_COMPARISON_OPS: dict[str, str] = {
+ "==": "=",
+ "!=": "<>",
+ ">": ">",
+ ">=": ">=",
+ "<": "<",
+ "<=": "<=",
+}
+
+SimilarityFunction = Literal["cosine", "euclidean"]
+
+
+class FalkorDBDocumentStore(DocumentStore):
+ """
+ A Haystack DocumentStore backed by FalkorDB — a high-performance graph database.
+
+ Optimised for GraphRAG workloads.
+
+ Documents are stored as graph nodes (labelled `Document` by default) in a named
+ FalkorDB graph. Document properties, including `meta` fields, are stored
+ **flat** at the same level as `id` and `content` — exactly the same layout as
+ the `neo4j-haystack` reference integration.
+
+ Vector search is performed via FalkorDB's native vector index —
+ **no APOC is required**. All bulk writes use `UNWIND` + `MERGE` for safe,
+ idiomatic OpenCypher upserts.
+
+ Usage example:
+
+ ```python
+ from haystack_integrations.document_stores.falkordb import FalkorDBDocumentStore
+ from haystack.dataclasses import Document
+
+ store = FalkorDBDocumentStore(host="localhost", port=6379)
+ store.write_documents([
+ Document(content="Hello, GraphRAG!", meta={"year": 2024}),
+ ])
+ print(store.count_documents()) # 1
+ ```
+ """
+
+ def __init__(
+ self,
+ *,
+ host: str = "localhost",
+ port: int = 6379,
+ graph_name: str = "haystack",
+ username: str | None = None,
+ password: Secret | None = None,
+ node_label: str = "Document",
+ embedding_dim: int = 768,
+ embedding_field: str = "embedding",
+ similarity: SimilarityFunction = "cosine",
+ write_batch_size: int = 100,
+ recreate_graph: bool = False,
+ verify_connectivity: bool = False,
+ ) -> None:
+ """
+ Create a new FalkorDBDocumentStore.
+
+ :param host: Hostname of the FalkorDB server.
+ :param port: Port the FalkorDB server listens on.
+ :param graph_name: Name of the FalkorDB graph to use. Each graph is an isolated
+ namespace.
+ :param username: Optional username for FalkorDB authentication.
+ :param password: Optional :class:`haystack.utils.Secret` holding the FalkorDB
+ password. The secret value is resolved lazily on first connection.
+ :param node_label: Label used for document nodes in the graph.
+ :param embedding_dim: Dimensionality of the vector embeddings. Used when
+ creating the vector index.
+ :param embedding_field: Name of the node property that stores the embedding
+ vector.
+ :param similarity: Similarity function for the vector index. Accepted values
+ are `"cosine"` and `"euclidean"`.
+ :param write_batch_size: Number of documents written per `UNWIND` batch.
+ :param recreate_graph: When `True` the existing graph (and all its data) is
+ dropped and recreated on initialisation. Useful for tests.
+ :param verify_connectivity: When `True` a connectivity probe is run
+ immediately in `__init__` — raises if the server is unreachable.
+ :raises ValueError: If `similarity` is not `"cosine"` or `"euclidean"`.
+ """
+ if similarity not in ("cosine", "euclidean"):
+ msg = (
+ f"Provided similarity '{similarity}' is not supported by FalkorDBDocumentStore. "
+ "Please choose one of: 'cosine', 'euclidean'."
+ )
+ raise ValueError(msg)
+
+ self.host = host
+ self.port = port
+ self.graph_name = graph_name
+ self.username = username
+ self.password = password
+ self.node_label = node_label
+ self.embedding_dim = embedding_dim
+ self.embedding_field = embedding_field
+ self.similarity: SimilarityFunction = similarity
+ self.write_batch_size = write_batch_size
+ self.recreate_graph = recreate_graph
+ self.verify_connectivity = verify_connectivity
+
+ # Lazy — populated on first use via ensure_connected().
+ self.client: Any = None
+ self.graph: Any = None
+ self.initialized: bool = False
+
+ if verify_connectivity:
+ self._ensure_connected()
+
+ def to_dict(self) -> dict[str, Any]:
+ """Serialize this document store to a dictionary."""
+ return default_to_dict(
+ self,
+ host=self.host,
+ port=self.port,
+ graph_name=self.graph_name,
+ username=self.username,
+ password=self.password,
+ node_label=self.node_label,
+ embedding_dim=self.embedding_dim,
+ embedding_field=self.embedding_field,
+ similarity=self.similarity,
+ write_batch_size=self.write_batch_size,
+ recreate_graph=self.recreate_graph,
+ verify_connectivity=self.verify_connectivity,
+ )
+
+ @classmethod
+ def from_dict(cls, data: dict[str, Any]) -> FalkorDBDocumentStore:
+ """Deserialize a document store from a dictionary."""
+ return default_from_dict(cls, data)
+
+ # ------------------------------------------------------------------
+ # Internal connection helpers
+ # ------------------------------------------------------------------
+
+ def _ensure_connected(self) -> None:
+ """
+ Lazily open the FalkorDB connection and set up the graph schema.
+
+ Called at the start of every public method so the store remains
+ serialisable without an active database connection.
+ """
+ if self.initialized:
+ return
+
+ password_value = self.password.resolve_value() if self.password is not None else None
+
+ self.client = falkordb.FalkorDB(
+ host=self.host,
+ port=self.port,
+ username=self.username,
+ password=password_value,
+ )
+
+ if self.recreate_graph:
+ try:
+ # In falkordb-py, delete() is a method of the Graph object
+ self.client.select_graph(self.graph_name).delete()
+ except Exception:
+ logger.debug("Graph '%s' could not be deleted (may not exist yet).", self.graph_name)
+
+ self.graph = self.client.select_graph(self.graph_name)
+ self._ensure_schema()
+ self.initialized = True
+
+ def _ensure_schema(self) -> None:
+ """
+ Create the property index and vector index if they do not already exist.
+
+ Uses only standard OpenCypher / FalkorDB-native syntax — **no APOC**.
+ """
+ # Property index on (:node_label {id}) for fast MERGE lookups.
+ try:
+ self.graph.query(f"CREATE INDEX FOR (d:{self.node_label}) ON (d.id)")
+ except ResponseError as e:
+ if "already indexed" in str(e).lower() or "already exists" in str(e).lower():
+ logger.debug("Property index on %s(id) already exists — skipping creation.", self.node_label)
+ else:
+ raise e
+
+ # FalkorDB-native vector index syntax
+ try:
+ cypher = (
+ f"CREATE VECTOR INDEX FOR (d:{self.node_label}) "
+ f"ON (d.{self.embedding_field}) "
+ f"OPTIONS {{dimension: {self.embedding_dim}, similarityFunction: '{self.similarity}'}}"
+ )
+ self.graph.query(cypher)
+ except ResponseError as e:
+ if "already indexed" in str(e).lower() or "already exists" in str(e).lower():
+ logger.debug(
+ "Vector index on %s(%s) already exists — skipping creation.",
+ self.node_label,
+ self.embedding_field,
+ )
+ else:
+ raise e
+
+ # ------------------------------------------------------------------
+ # Haystack DocumentStore protocol
+ # ------------------------------------------------------------------
+
+ def count_documents(self) -> int:
+ """
+ Return the number of documents currently stored in the graph.
+
+ :returns: Integer count of document nodes.
+ """
+ self._ensure_connected()
+ result = self.graph.query(f"MATCH (d:{self.node_label}) RETURN count(d) AS n")
+ rows = result.result_set
+ return int(rows[0][0]) if rows else 0
+
+ def filter_documents(self, filters: dict[str, Any] | None = None) -> list[Document]:
+ """
+ Retrieve all documents that match the provided Haystack filters.
+
+ :param filters: Optional Haystack filter dict. When `None` all documents are
+ returned. For filter syntax see
+ [Metadata filtering](https://docs.haystack.deepset.ai/docs/metadata-filtering)
+ :returns: List of matching :class:`haystack.dataclasses.Document` objects.
+ :raises ValueError: If the filter dict is malformed.
+ """
+ self._ensure_connected()
+ if not filters:
+ result = self.graph.query(f"MATCH (d:{self.node_label}) RETURN d ORDER BY d.id")
+ return [_node_to_document(row[0]) for row in result.result_set]
+
+ if "operator" not in filters:
+ msg = "Invalid filter syntax. See https://docs.haystack.deepset.ai/docs/metadata-filtering"
+ raise FilterError(msg)
+
+ where_clause, params = _convert_filters(filters)
+ cypher = f"MATCH (d:{self.node_label}) WHERE {where_clause} RETURN d ORDER BY d.id"
+
+ result = self.graph.query(cypher, params)
+ return [_node_to_document(row[0]) for row in result.result_set]
+
+ def write_documents(
+ self,
+ documents: list[Document],
+ policy: DuplicatePolicy = DuplicatePolicy.NONE,
+ ) -> int:
+ """
+ Write documents to the FalkorDB graph using `UNWIND` + `MERGE` for batching.
+
+ Document `meta` fields are stored **flat** at the same level as `id` and
+ `content` — no prefix is added. This matches the layout used by the
+ `neo4j-haystack` reference integration.
+
+ :param documents: List of :class:`haystack.dataclasses.Document` objects.
+ :param policy: How to handle documents whose `id` already exists.
+ Defaults to :attr:`DuplicatePolicy.NONE` (treated as FAIL).
+ :raises ValueError: If `documents` contains non-Document elements.
+ :raises DuplicateDocumentError: If `policy` is FAIL / NONE and a duplicate
+ ID is encountered.
+ :raises DocumentStoreError: If any other DB error occurs.
+ :returns: Number of documents written or updated.
+ """
+ self._ensure_connected()
+
+ for doc in documents:
+ if not isinstance(doc, Document):
+ msg = f"write_documents() expects a list of Documents but got an element of type {type(doc)}."
+ raise ValueError(msg)
+
+ if not documents:
+ logger.warning("Calling FalkorDBDocumentStore.write_documents() with an empty list.")
+ return 0
+
+ if policy == DuplicatePolicy.NONE:
+ policy = DuplicatePolicy.FAIL
+
+ document_objects = self._handle_duplicate_documents(documents, policy)
+
+ written = 0
+ for batch_start in range(0, len(document_objects), self.write_batch_size):
+ batch = document_objects[batch_start : batch_start + self.write_batch_size]
+ written += self._write_batch(batch, policy)
+
+ return written
+
+ def _handle_duplicate_documents(
+ self,
+ documents: list[Document],
+ policy: DuplicatePolicy,
+ ) -> list[Document]:
+ """
+ Checks for IDs that already exist in the database.
+
+ :param documents: All documents to write.
+ :param policy: Duplicate handling policy.
+ :returns: Filtered list ready for batch writing.
+ :raises DuplicateDocumentError: When `policy` is FAIL and existing IDs found.
+ """
+ if policy in (DuplicatePolicy.SKIP, DuplicatePolicy.FAIL):
+ # Step 1: deduplicate within the incoming list itself.
+ documents = self._drop_duplicate_documents(documents)
+
+ # Step 2: find which IDs already exist in the DB.
+ ids = [doc.id for doc in documents]
+ existing = self.graph.query(
+ f"UNWIND $ids AS id MATCH (d:{self.node_label} {{id: id}}) RETURN d.id",
+ {"ids": ids},
+ )
+ ids_exist_in_db: list[str] = [row[0] for row in existing.result_set]
+
+ if ids_exist_in_db and policy == DuplicatePolicy.FAIL:
+ msg = f"Document with ids '{', '.join(ids_exist_in_db)}' already exists in graph '{self.graph_name}'."
+ raise DuplicateDocumentError(msg)
+
+ # For SKIP: remove those that already exist.
+ if ids_exist_in_db:
+ existing_set = set(ids_exist_in_db)
+ documents = [d for d in documents if d.id not in existing_set]
+
+ return documents
+
+ def _drop_duplicate_documents(self, documents: list[Document]) -> list[Document]:
+ """
+ Drop duplicate documents (by ID) within the provided list.
+
+ :param documents: Input list — may contain repeated IDs.
+ :returns: Deduplicated list preserving first-occurrence order.
+ """
+ seen_ids: set[str] = set()
+ unique: list[Document] = []
+ for doc in documents:
+ if doc.id in seen_ids:
+ logger.info(
+ "Duplicate Documents: Document with id '%s' already present in the batch — skipping.",
+ doc.id,
+ )
+ continue
+ unique.append(doc)
+ seen_ids.add(doc.id)
+ return unique
+
+ def _write_batch(self, documents: list[Document], policy: DuplicatePolicy) -> int:
+ """
+ Write a single batch of documents using a single UNWIND query.
+
+ By the time this is called, duplicate handling has already been performed by
+ :meth:`_handle_duplicate_documents`.
+
+ :param documents: Batch of Documents (≤ `write_batch_size`).
+ :param policy: Duplicate policy — only OVERWRITE needs a different Cypher template.
+ :returns: Number of nodes created or updated.
+ """
+ records = [_document_to_falkordb_record(doc) for doc in documents]
+
+ if policy == DuplicatePolicy.OVERWRITE:
+ # ON MATCH SET applies the full map (including updated fields).
+ cypher = f"""
+UNWIND $docs AS doc
+MERGE (d:{self.node_label} {{id: doc.id}})
+ON CREATE SET d += doc
+ON MATCH SET d += doc
+FOREACH (x IN CASE WHEN doc.{self.embedding_field} IS NOT NULL THEN [1] ELSE [] END |
+ SET d.{self.embedding_field} = vecf32(doc.{self.embedding_field})
+)
+RETURN count(d) AS n
+"""
+ else:
+ # FAIL already filtered duplicates above; SKIP excluded them.
+ # In both remaining cases we only write truly-new nodes.
+ cypher = f"""
+UNWIND $docs AS doc
+MERGE (d:{self.node_label} {{id: doc.id}})
+ON CREATE SET d += doc
+FOREACH (x IN CASE WHEN doc.{self.embedding_field} IS NOT NULL THEN [1] ELSE [] END |
+ SET d.{self.embedding_field} = vecf32(doc.{self.embedding_field})
+)
+RETURN count(d) AS n
+"""
+
+ try:
+ result = self.graph.query(cypher, {"docs": records})
+ rows = result.result_set
+ return int(rows[0][0]) if rows else 0
+ except Exception as exc:
+ msg = f"Failed to write documents to FalkorDB: {exc}"
+ raise DocumentStoreError(msg) from exc
+
+ def delete_documents(self, document_ids: list[str]) -> None:
+ """
+ Delete documents by their IDs using a single `UNWIND`-based query.
+
+ :param document_ids: List of document IDs to remove from the graph.
+ """
+ self._ensure_connected()
+ if not document_ids:
+ return
+ self.graph.query(
+ f"UNWIND $ids AS id MATCH (d:{self.node_label} {{id: id}}) DETACH DELETE d",
+ {"ids": document_ids},
+ )
+
+ # ------------------------------------------------------------------
+ # Internal retrieval helpers (called by retriever components)
+ # ------------------------------------------------------------------
+
+ def _embedding_retrieval(
+ self,
+ query_embedding: list[float],
+ top_k: int = 10,
+ filters: dict[str, Any] | None = None,
+ scale_score: bool = True,
+ ) -> list[Document]:
+ """
+ Retrieve documents by vector similarity using FalkorDB's native vector index.
+
+ Uses `CALL db.idx.vector.queryNodes` — FalkorDB's OpenCypher extension for
+ ANN search. **No APOC is required.**
+
+ Cosine scores are returned in `[-1, 1]`; when `scale_score=True` they are
+ scaled to `[0, 1]` using the formula:
+ `(score + 1) / 2`. Euclidean scores are transformed with a sigmoid.
+
+ :param query_embedding: Query vector as a plain Python list of floats.
+ :param top_k: Maximum number of results to return.
+ :param filters: Optional Haystack filters applied as a `WHERE` predicate
+ on the vector search result set (post-filter).
+ :param scale_score: Whether to scale the raw similarity score to `[0, 1]`.
+ :returns: List of :class:`Document` objects ordered by similarity (best first).
+ """
+ self._ensure_connected()
+
+ if filters:
+ where_clause, filter_params = _convert_filters(filters)
+ cypher = f"""
+CALL db.idx.vector.queryNodes('{self.node_label}', '{self.embedding_field}', $top_k, vecf32($query_embedding))
+YIELD node AS d, score
+WHERE {where_clause}
+RETURN d, score
+ORDER BY score DESC
+"""
+ params: dict[str, Any] = {
+ "top_k": top_k,
+ "query_embedding": query_embedding,
+ **filter_params,
+ }
+ else:
+ cypher = f"""
+CALL db.idx.vector.queryNodes('{self.node_label}', '{self.embedding_field}', $top_k, vecf32($query_embedding))
+YIELD node AS d, score
+RETURN d, score
+ORDER BY score DESC, d.id ASC
+"""
+ params = {"top_k": top_k, "query_embedding": query_embedding}
+
+ result = self.graph.query(cypher, params)
+ documents = []
+ for row in result.result_set:
+ node, score = row[0], row[1]
+ doc = _node_to_document(node)
+ final_score = self._scale_to_unit_interval(float(score)) if scale_score else float(score)
+ doc = replace(doc, score=final_score)
+ documents.append(doc)
+ return documents
+
+ def _cypher_retrieval(
+ self,
+ cypher_query: str,
+ parameters: dict[str, Any] | None = None,
+ ) -> list[Document]:
+ """
+ Execute an arbitrary OpenCypher query and map the results to Documents.
+
+ The first element of each result row is converted to a
+ :class:`haystack.dataclasses.Document`.
+
+ :param cypher_query: A valid OpenCypher query string.
+ :param parameters: Optional query parameters (`$param` placeholders).
+ :returns: List of :class:`Document` objects built from the query results.
+ :raises DocumentStoreError: If the query fails.
+ """
+ self._ensure_connected()
+ try:
+ # We don't force ORDER BY here as the query is custom,
+ # but we ensured everything else is stable.
+ result = self.graph.query(cypher_query, parameters or {})
+ return [_node_to_document(row[0]) for row in result.result_set]
+ except Exception as exc:
+ msg = f"Cypher query failed: {exc}"
+ raise DocumentStoreError(msg) from exc
+
+ def _scale_to_unit_interval(self, score: float) -> float:
+ """
+ Scale a raw similarity score to the unit interval `[0, 1]`.
+
+ Uses the following formulas:
+ - Cosine: `(score + 1) / 2`
+ - Euclidean: sigmoid `1 / (1 + exp(-score / 100))`
+
+ :param score: Raw score returned by the vector index.
+ :returns: Scaled score in `[0, 1]`.
+ """
+ if self.similarity == "cosine":
+ return (score + 1) / 2
+ return float(1 / (1 + math.exp(-score / 100)))
+
+
+# ---------------------------------------------------------------------------
+# Module-level helpers
+# ---------------------------------------------------------------------------
+
+
+def _document_to_falkordb_record(doc: Document) -> dict[str, Any]:
+ """
+ Convert a Haystack Document to a flat dict for storage as FalkorDB node properties.
+
+ - `meta` fields are stored **at the same level** as `id` and `content`.
+ - `id`, `content`, `embedding` are top-level.
+ - All other metadata keys are flattened into the root.
+
+ :param doc: The document to convert.
+ :returns: Flat dictionary of node properties.
+ """
+ record = {
+ "id": doc.id,
+ "content": doc.content,
+ "embedding": doc.embedding,
+ }
+ if doc.meta:
+ record.update(doc.meta)
+
+ # Filter out None values — FalkorDB nodes don't need null properties stored.
+ return {k: v for k, v in record.items() if v is not None}
+
+
+def _node_to_document(node: Any) -> Document:
+ """
+ Convert a FalkorDB graph node back to a Haystack Document.
+
+ Properties that are not part of the standard Document schema are moved
+ into the `meta` dictionary.
+
+ :param node: A FalkorDB `Node` object or a plain `dict`.
+ :returns: Reconstructed :class:`haystack.dataclasses.Document`.
+ """
+ if hasattr(node, "properties"):
+ record: dict[str, Any] = dict(node.properties)
+ elif isinstance(node, dict):
+ record = node
+ else:
+ record = {}
+
+ # Standard Document fields
+ doc_id = record.pop("id", None)
+ content = record.pop("content", None)
+ embedding = record.pop("embedding", None)
+ score = record.pop("score", None)
+
+ # Everything else is metadata
+ # sparse_embedding is also popped if present (not supported by falkordb yet)
+ record.pop("sparse_embedding", None)
+
+ return Document(id=doc_id, content=content, embedding=embedding, meta=record, score=score)
+
+
+def _convert_filters(filters: dict[str, Any]) -> tuple[str, dict[str, Any]]:
+ """
+ Translate a Haystack filter dict into an OpenCypher `WHERE` sub-expression.
+
+ Supports the full Haystack filter DSL:
+
+ - Logical: `AND`, `OR`, `NOT`
+ - Comparison: `==`, `!=`, `>`, `>=`, `<`, `<=`
+ - Membership: `in`, `not in`
+
+ All values are passed as named query parameters to prevent injection.
+
+ :param filters: A Haystack filter dictionary.
+ :returns: Tuple of `(where_clause_string, params_dict)`.
+ :raises ValueError: If an unsupported operator or malformed filter is provided.
+ """
+ params: dict[str, Any] = {}
+ clause = _build_clause(filters, params, counter=[0])
+ return clause, params
+
+
+def _build_clause(node: dict[str, Any], params: dict[str, Any], counter: list[int]) -> str:
+ """
+ Recursively build a Cypher WHERE sub-expression from a Haystack filter node.
+
+ :param node: A filter node (logical group or comparison leaf).
+ :param params: Accumulating query parameter dict (mutated in place).
+ :param counter: Single-element list used as a mutable integer for unique param names.
+ :returns: Cypher expression string.
+ """
+ operator = node.get("operator", "")
+
+ # ------------------------------------------------------------------
+ # Logical / grouping operators
+ # ------------------------------------------------------------------
+ if operator.upper() in ("AND", "OR"):
+ if "conditions" not in node:
+ msg = f"Logical operator '{operator}' requires a 'conditions' key"
+ raise FilterError(msg)
+ sub_clauses = [_build_clause(c, params, counter) for c in node["conditions"]]
+ joiner = f" {operator.upper()} "
+ return f"({joiner.join(sub_clauses)})"
+
+ if operator.upper() == "NOT":
+ if "conditions" not in node:
+ msg = "Logical operator 'NOT' requires a 'conditions' key"
+ raise FilterError(msg)
+ sub_clauses = [_build_clause(c, params, counter) for c in node["conditions"]]
+ inner = " AND ".join(sub_clauses)
+ return f"NOT ({inner})"
+
+ # ------------------------------------------------------------------
+ # Leaf (comparison / membership) operators
+ # ------------------------------------------------------------------
+ if "field" not in node:
+ msg = f"Comparison operator '{operator}' requires a 'field' key"
+ raise FilterError(msg)
+ if "value" not in node:
+ msg = f"Comparison operator '{operator}' requires a 'value' key"
+ raise FilterError(msg)
+
+ field: str = node["field"]
+ value: Any = node["value"]
+
+ # Because meta fields are stored flat (no prefix), all fields map to d..
+ # We strip 'meta.' from the field name if Haystack adds it.
+ actual_field = field[5:] if field.startswith("meta.") else field
+ cypher_field = f"d.{actual_field}"
+
+ param_name = f"p{counter[0]}"
+ counter[0] += 1
+
+ if operator == "==":
+ if value is None:
+ return f"{cypher_field} IS NULL"
+ params[param_name] = value
+ return f"coalesce({cypher_field} = ${param_name}, false)"
+
+ if operator == "!=":
+ if value is None:
+ return f"{cypher_field} IS NOT NULL"
+ params[param_name] = value
+ return f"coalesce({cypher_field} <> ${param_name}, true)"
+
+ if operator in _COMPARISON_OPS:
+ if value is None:
+ return "false"
+ if isinstance(value, list):
+ msg = f"Operator '{operator}' does not support list values"
+ raise FilterError(msg)
+ if isinstance(value, str):
+ try:
+ datetime.fromisoformat(value)
+ except ValueError:
+ msg = f"Operator '{operator}' requires a numeric or ISO date value, got non-ISO string: '{value}'"
+ raise FilterError(msg) from None
+ params[param_name] = value
+ return f"coalesce({cypher_field} {_COMPARISON_OPS[operator]} ${param_name}, false)"
+
+ if operator == "in":
+ if not isinstance(value, list):
+ msg = f"Operator 'in' requires a list value, got {type(value).__name__}"
+ raise FilterError(msg)
+ params[param_name] = value
+ return f"coalesce({cypher_field} IN ${param_name}, false)"
+
+ if operator == "not in":
+ if not isinstance(value, list):
+ msg = f"Operator 'not in' requires a list value, got {type(value).__name__}"
+ raise FilterError(msg)
+ params[param_name] = value
+ return f"coalesce(NOT ({cypher_field} IN ${param_name}), true)"
+
+ msg = f"Unsupported filter operator: '{operator}'"
+ raise FilterError(msg)
diff --git a/integrations/falkordb/src/haystack_integrations/document_stores/py.typed b/integrations/falkordb/src/haystack_integrations/document_stores/py.typed
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/integrations/falkordb/tests/__init__.py b/integrations/falkordb/tests/__init__.py
new file mode 100644
index 0000000000..c1764a6e03
--- /dev/null
+++ b/integrations/falkordb/tests/__init__.py
@@ -0,0 +1,3 @@
+# SPDX-FileCopyrightText: 2022-present deepset GmbH
+#
+# SPDX-License-Identifier: Apache-2.0
diff --git a/integrations/falkordb/tests/test_document_store.py b/integrations/falkordb/tests/test_document_store.py
new file mode 100644
index 0000000000..2aacd82eef
--- /dev/null
+++ b/integrations/falkordb/tests/test_document_store.py
@@ -0,0 +1,120 @@
+# SPDX-FileCopyrightText: 2024-present deepset GmbH
+#
+# SPDX-License-Identifier: Apache-2.0
+
+import logging
+import os
+
+import pytest
+from haystack.dataclasses import Document
+from haystack.testing.document_store import DocumentStoreBaseTests
+
+from haystack_integrations.components.retrievers.falkordb import (
+ FalkorDBCypherRetriever,
+ FalkorDBEmbeddingRetriever,
+)
+from haystack_integrations.document_stores.falkordb import FalkorDBDocumentStore
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.mark.integration
+class TestDocumentStore(DocumentStoreBaseTests):
+ """
+ Test FalkorDBDocumentStore against the standard Haystack DocumentStore tests.
+ """
+
+ @staticmethod
+ def assert_documents_are_equal(received: list[Document], expected: list[Document]):
+ """
+ FalkorDB stores embeddings as vecf32 (float32), so exact float64 round-trip
+ equality is not possible. Sort both lists by id to compensate for non-deterministic
+ graph traversal order, and compare only id/content/meta plus embedding presence.
+ """
+ assert len(received) == len(expected), f"Expected {len(expected)} documents but got {len(received)}"
+ received_sorted = sorted(received, key=lambda d: d.id)
+ expected_sorted = sorted(expected, key=lambda d: d.id)
+ for recv, exp in zip(received_sorted, expected_sorted, strict=True):
+ assert recv.id == exp.id
+ assert recv.content == exp.content
+ assert recv.meta == exp.meta
+ assert (recv.embedding is None) == (exp.embedding is None)
+
+ @pytest.fixture
+ def document_store(self, request):
+ host = os.environ.get("FALKORDB_HOST", "localhost")
+ port = int(os.environ.get("FALKORDB_PORT", "6379"))
+
+ # Use a unique graph name for each test to avoid interference
+ graph_name = f"test_graph_{request.node.name[:30]}"
+ store = FalkorDBDocumentStore(
+ host=host,
+ port=port,
+ graph_name=graph_name,
+ embedding_dim=768,
+ recreate_graph=True,
+ verify_connectivity=True,
+ )
+ yield store
+ # Teardown: delete the graph
+ try:
+ store.client.select_graph(graph_name).delete()
+ except Exception:
+ logger.debug("Could not delete graph %s during teardown", graph_name)
+
+ def test_write_documents(self, document_store):
+ """
+ Test write_documents() default behaviour.
+ """
+ doc = Document(content="test doc")
+ assert document_store.write_documents([doc]) == 1
+ self.assert_documents_are_equal(document_store.filter_documents(), [doc])
+
+ @pytest.fixture
+ def embedding_store(self):
+ host = os.environ.get("FALKORDB_HOST", "localhost")
+ port = int(os.environ.get("FALKORDB_PORT", "6379"))
+ store = FalkorDBDocumentStore(
+ host=host,
+ port=port,
+ graph_name="test_embedding_retrieval",
+ embedding_dim=3,
+ recreate_graph=True,
+ verify_connectivity=True,
+ )
+ yield store
+ try:
+ store.client.select_graph("test_embedding_retrieval").delete()
+ except Exception:
+ logger.debug("Could not delete graph test_embedding_retrieval during teardown")
+
+ def test_embedding_retrieval(self, embedding_store):
+ docs = [
+ Document(content="Graph databases represent data as nodes and edges.", embedding=[0.1, 0.2, 0.3]),
+ Document(content="Large language models generate text.", embedding=[0.9, 0.8, 0.1]),
+ ]
+ embedding_store.write_documents(docs)
+
+ retriever = FalkorDBEmbeddingRetriever(document_store=embedding_store, top_k=1)
+ res = retriever.run(query_embedding=[0.1, 0.25, 0.3])
+
+ assert len(res["documents"]) == 1
+ assert "Graph databases" in res["documents"][0].content
+ assert res["documents"][0].score is not None
+
+ def test_cypher_retriever_graph_traversal(self, document_store):
+ document_store.graph.query(
+ "CREATE (a:Document {id: 'docA', content: 'Node A'})"
+ "-[:REFERENCES]->"
+ "(b:Document {id: 'docB', content: 'Node B'})"
+ )
+
+ retriever = FalkorDBCypherRetriever(
+ document_store=document_store,
+ custom_cypher_query=("MATCH (:Document {id: $source_id})-[:REFERENCES]->(target:Document) RETURN target"),
+ )
+ res = retriever.run(parameters={"source_id": "docA"})
+
+ assert len(res["documents"]) == 1
+ assert res["documents"][0].id == "docB"
+ assert res["documents"][0].content == "Node B"
diff --git a/integrations/falkordb/tests/test_retrievers.py b/integrations/falkordb/tests/test_retrievers.py
new file mode 100644
index 0000000000..ef970c30bf
--- /dev/null
+++ b/integrations/falkordb/tests/test_retrievers.py
@@ -0,0 +1,161 @@
+# SPDX-FileCopyrightText: 2024-present deepset GmbH
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from unittest.mock import MagicMock
+
+import pytest
+from haystack.core.errors import DeserializationError
+from haystack.dataclasses import Document
+from haystack.document_stores.types.filter_policy import FilterPolicy
+
+from haystack_integrations.components.retrievers.falkordb import (
+ FalkorDBCypherRetriever,
+ FalkorDBEmbeddingRetriever,
+)
+from haystack_integrations.document_stores.falkordb import FalkorDBDocumentStore
+
+
+class TestFalkorDBEmbeddingRetriever:
+ def test_init_invalid_store(self):
+ with pytest.raises(ValueError, match="must be an instance of FalkorDBDocumentStore"):
+ FalkorDBEmbeddingRetriever(document_store=MagicMock()) # type: ignore
+
+ def test_run(self):
+ store = MagicMock(spec=FalkorDBDocumentStore)
+ expected_docs = [Document(content="doc1"), Document(content="doc2")]
+ store._embedding_retrieval.return_value = expected_docs
+
+ retriever = FalkorDBEmbeddingRetriever(document_store=store)
+ res = retriever.run(query_embedding=[0.1, 0.2])
+
+ store._embedding_retrieval.assert_called_once_with(
+ query_embedding=[0.1, 0.2],
+ top_k=10,
+ filters=None,
+ )
+ assert res["documents"] == expected_docs
+
+ def test_filter_policy_replace(self):
+ store = MagicMock(spec=FalkorDBDocumentStore)
+ retriever = FalkorDBEmbeddingRetriever(
+ document_store=store,
+ filters={"field": "year", "operator": "==", "value": 2020},
+ filter_policy=FilterPolicy.REPLACE,
+ )
+
+ runtime_filters = {"field": "author", "operator": "==", "value": "Alice"}
+ retriever.run(query_embedding=[0.1], filters=runtime_filters)
+
+ # REPLACE policy means runtime filters completely replace init filters
+ store._embedding_retrieval.assert_called_once_with(
+ query_embedding=[0.1],
+ top_k=10,
+ filters=runtime_filters,
+ )
+
+ def test_filter_policy_merge(self):
+ store = MagicMock(spec=FalkorDBDocumentStore)
+ retriever = FalkorDBEmbeddingRetriever(
+ document_store=store,
+ filters={"field": "year", "operator": "==", "value": 2020},
+ filter_policy=FilterPolicy.MERGE,
+ )
+
+ runtime_filters = {"field": "author", "operator": "==", "value": "Alice"}
+ retriever.run(query_embedding=[0.1], filters=runtime_filters)
+
+ called_filters = store._embedding_retrieval.call_args[1]["filters"]
+ # MERGE policy nests them in an AND
+ assert called_filters["operator"] == "AND"
+ assert len(called_filters["conditions"]) == 2
+
+ def test_to_dict_from_dict(self):
+ store = MagicMock(spec=FalkorDBDocumentStore)
+ store.to_dict.return_value = {"type": "FalkorDBDocumentStore", "init_parameters": {}}
+
+ retriever = FalkorDBEmbeddingRetriever(
+ document_store=store,
+ filters={"field": "year", "operator": "==", "value": 2020},
+ top_k=5,
+ filter_policy=FilterPolicy.MERGE,
+ )
+ data = retriever.to_dict()
+ assert data["init_parameters"]["top_k"] == 5
+ assert data["init_parameters"]["filter_policy"] == "merge"
+
+ # We can't properly instantiate the mock in from_dict, so we just verify to_dict structure here
+ # or we use a real store for the roundtrip:
+ store_real = FalkorDBDocumentStore(verify_connectivity=False)
+ retriever_real = FalkorDBEmbeddingRetriever(
+ document_store=store_real,
+ filters={"field": "year", "operator": "==", "value": 2020},
+ top_k=5,
+ filter_policy=FilterPolicy.MERGE,
+ )
+ data_real = retriever_real.to_dict()
+ new_retriever = FalkorDBEmbeddingRetriever.from_dict(data_real)
+ assert new_retriever.top_k == 5
+ assert new_retriever.filter_policy == FilterPolicy.MERGE
+
+ def test_from_dict_without_document_store(self):
+ data = {"type": "FalkorDBEmbeddingRetriever", "init_parameters": {}}
+ with pytest.raises(DeserializationError):
+ FalkorDBEmbeddingRetriever.from_dict(data)
+
+
+class TestFalkorDBCypherRetriever:
+ def test_init_invalid_store(self):
+ with pytest.raises(ValueError, match="must be an instance of FalkorDBDocumentStore"):
+ FalkorDBCypherRetriever(document_store=MagicMock()) # type: ignore
+
+ def test_run_with_init_query(self):
+ store = MagicMock(spec=FalkorDBDocumentStore)
+ expected_docs = [Document(content="doc1")]
+ store._cypher_retrieval.return_value = expected_docs
+
+ retriever = FalkorDBCypherRetriever(document_store=store, custom_cypher_query="MATCH (d:Doc) RETURN d")
+ res = retriever.run(parameters={"a": 1})
+
+ store._cypher_retrieval.assert_called_once_with(
+ cypher_query="MATCH (d:Doc) RETURN d",
+ parameters={"a": 1},
+ )
+ assert res["documents"] == expected_docs
+
+ def test_run_with_runtime_query(self):
+ store = MagicMock(spec=FalkorDBDocumentStore)
+ store._cypher_retrieval.return_value = []
+
+ retriever = FalkorDBCypherRetriever(document_store=store, custom_cypher_query="MATCH (d:Doc) RETURN d")
+ # Runtime query overrides init query
+ retriever.run(query="MATCH (d:Other) RETURN d")
+
+ store._cypher_retrieval.assert_called_once_with(
+ cypher_query="MATCH (d:Other) RETURN d",
+ parameters=None,
+ )
+
+ def test_run_no_query_raises(self):
+ store = MagicMock(spec=FalkorDBDocumentStore)
+ retriever = FalkorDBCypherRetriever(document_store=store)
+
+ with pytest.raises(ValueError, match="query string must be provided"):
+ retriever.run()
+
+ def test_to_dict_from_dict(self):
+ store_real = FalkorDBDocumentStore(verify_connectivity=False)
+ retriever = FalkorDBCypherRetriever(
+ document_store=store_real,
+ custom_cypher_query="MATCH (d) RETURN d",
+ )
+ data = retriever.to_dict()
+ assert data["init_parameters"]["custom_cypher_query"] == "MATCH (d) RETURN d"
+
+ new_retriever = FalkorDBCypherRetriever.from_dict(data)
+ assert new_retriever.custom_cypher_query == "MATCH (d) RETURN d"
+
+ def test_from_dict_without_document_store(self):
+ data = {"type": "FalkorDBCypherRetriever", "init_parameters": {}}
+ with pytest.raises(DeserializationError):
+ FalkorDBCypherRetriever.from_dict(data)