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https://github.com/hwchase17/langchain
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langchain[patch]: return formatted SPARQL query on demand (#11263)
- **Description:** Added the `return_sparql_query` feature to the `GraphSparqlQAChain` class, allowing users to get the formatted SPARQL query along with the chain's result. - **Issue:** NA - **Dependencies:** None Note: I've ensured that the PR passes linting and testing by running make format, make lint, and make test locally. I have added a test for the integration (which relies on network access) and I have added an example to the notebook showing its use.
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@ -21,7 +21,7 @@
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"cell_type": "code",
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@ -37,7 +37,7 @@
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 6,
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"id": "1fe76ccd",
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"metadata": {},
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"outputs": [
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@ -121,7 +121,7 @@
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": null,
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"id": "7476ce98",
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@ -250,7 +250,7 @@
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 27,
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"id": "f874171b",
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"metadata": {},
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"outputs": [
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@ -277,13 +277,99 @@
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")\n",
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"graph.query(query)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "eb00a625-a6c9-4766-b3f0-eaed024851c9",
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"metadata": {},
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"source": [
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"## Return SQARQL query\n",
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"You can return the SPARQL query step from the Sparql QA Chain using the `return_sparql_query` parameter"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"id": "f13e2865-176a-4417-95e6-db818b214d08",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = GraphSparqlQAChain.from_llm(\n",
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" ChatOpenAI(temperature=0), graph=graph, verbose=True, return_sparql_query=True\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"id": "4f4d47b6-4202-4e74-8c88-aeaac5344c04",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new GraphSparqlQAChain chain...\u001b[0m\n",
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"Identified intent:\n",
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"\u001b[32;1m\u001b[1;3mSELECT\u001b[0m\n",
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"Generated SPARQL:\n",
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"\u001b[32;1m\u001b[1;3mPREFIX foaf: <http://xmlns.com/foaf/0.1/>\n",
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"SELECT ?workHomepage\n",
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"WHERE {\n",
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" ?person foaf:name \"Tim Berners-Lee\" .\n",
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" ?person foaf:workplaceHomepage ?workHomepage .\n",
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"}\u001b[0m\n",
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"Full Context:\n",
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"\u001b[32;1m\u001b[1;3m[]\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n",
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"SQARQL query: PREFIX foaf: <http://xmlns.com/foaf/0.1/>\n",
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"SELECT ?workHomepage\n",
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"WHERE {\n",
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" ?person foaf:name \"Tim Berners-Lee\" .\n",
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" ?person foaf:workplaceHomepage ?workHomepage .\n",
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"}\n",
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"Final answer: Tim Berners-Lee's work homepage is http://www.w3.org/People/Berners-Lee/.\n"
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]
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}
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],
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"source": [
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"result = chain(\"What is Tim Berners-Lee's work homepage?\")\n",
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"print(f\"SQARQL query: {result['sparql_query']}\")\n",
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"print(f\"Final answer: {result['result']}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"id": "be3d9ff7-dc00-47d0-857d-fd40437a3f22",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"PREFIX foaf: <http://xmlns.com/foaf/0.1/>\n",
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"SELECT ?workHomepage\n",
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"WHERE {\n",
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" ?person foaf:name \"Tim Berners-Lee\" .\n",
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" ?person foaf:workplaceHomepage ?workHomepage .\n",
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"}\n"
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]
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}
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],
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"source": [
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"print(result[\"sparql_query\"])"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "lc",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "lc"
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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@ -295,7 +381,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.4"
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"version": "3.10.9"
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}
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},
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"nbformat": 4,
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@ -41,15 +41,25 @@ class GraphSparqlQAChain(Chain):
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sparql_generation_update_chain: LLMChain
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sparql_intent_chain: LLMChain
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qa_chain: LLMChain
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return_sparql_query: bool = False
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input_key: str = "query" #: :meta private:
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output_key: str = "result" #: :meta private:
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sparql_query_key: str = "sparql_query" #: :meta private:
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@property
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def input_keys(self) -> List[str]:
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"""Return the input keys.
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:meta private:
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"""
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return [self.input_key]
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@property
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def output_keys(self) -> List[str]:
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"""Return the output keys.
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:meta private:
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"""
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_output_keys = [self.output_key]
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return _output_keys
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@ -135,4 +145,8 @@ class GraphSparqlQAChain(Chain):
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res = "Successfully inserted triples into the graph."
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else:
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raise ValueError("Unsupported SPARQL query type.")
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return {self.output_key: res}
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chain_result: Dict[str, Any] = {self.output_key: res}
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if self.return_sparql_query:
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chain_result[self.sparql_query_key] = generated_sparql
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return chain_result
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@ -78,3 +78,29 @@ def test_sparql_insert() -> None:
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os.remove(_local_copy)
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except OSError:
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pass
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def test_sparql_select_return_query() -> None:
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"""
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Test for generating and executing simple SPARQL SELECT query
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and returning the generated SPARQL query.
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"""
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berners_lee_card = "http://www.w3.org/People/Berners-Lee/card"
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graph = RdfGraph(
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source_file=berners_lee_card,
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standard="rdf",
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)
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chain = GraphSparqlQAChain.from_llm(
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OpenAI(temperature=0), graph=graph, return_sparql_query=True
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)
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output = chain("What is Tim Berners-Lee's work homepage?")
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# Verify the expected answer
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expected_output = (
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" The work homepage of Tim Berners-Lee is "
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"http://www.w3.org/People/Berners-Lee/."
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)
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assert output["result"] == expected_output
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assert "sparql_query" in output
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