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community[patch]: update embeddings/oracleai.py (#22240)
Thank you for contributing to LangChain! - [ ] **PR title**: "package: description" - Where "package" is whichever of langchain, community, core, experimental, etc. is being modified. Use "docs: ..." for purely docs changes, "templates: ..." for template changes, "infra: ..." for CI changes. - Example: "community: add foobar LLM" "community/embeddings: update oracleai.py" - [ ] **PR message**: ***Delete this entire checklist*** and replace with - **Description:** a description of the change - **Issue:** the issue # it fixes, if applicable - **Dependencies:** any dependencies required for this change - **Twitter handle:** if your PR gets announced, and you'd like a mention, we'll gladly shout you out! Adding oracle VECTOR_ARRAY_T support. - [ ] **Add tests and docs**: If you're adding a new integration, please include 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/docs/integrations` directory. Tests are not impacted. - [ ] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. See contribution guidelines for more: https://python.langchain.com/docs/contributing/ Done. Additional guidelines: - Make sure optional dependencies are imported within a function. - Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests. - Most PRs should not touch more than one package. - Changes should be backwards compatible. - If you are adding something to community, do not re-import it in langchain. If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
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@@ -118,23 +118,29 @@ class OracleEmbeddings(BaseModel, Embeddings):
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"begin utl_http.set_proxy(:proxy); end;", proxy=self.proxy
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)
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for text in texts:
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cursor.execute(
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"select t.* "
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+ "from dbms_vector_chain.utl_to_embeddings(:content, "
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+ "json(:params)) t",
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content=text,
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params=json.dumps(self.params),
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)
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chunks = []
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for i, text in enumerate(texts, start=1):
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chunk = {"chunk_id": i, "chunk_data": text}
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chunks.append(json.dumps(chunk))
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for row in cursor:
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if row is None:
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embeddings.append([])
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else:
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rdata = json.loads(row[0])
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# dereference string as array
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vec = json.loads(rdata["embed_vector"])
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embeddings.append(vec)
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vector_array_type = self.conn.gettype("SYS.VECTOR_ARRAY_T")
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inputs = vector_array_type.newobject(chunks)
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cursor.execute(
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"select t.* "
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+ "from dbms_vector_chain.utl_to_embeddings(:content, "
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+ "json(:params)) t",
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content=inputs,
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params=json.dumps(self.params),
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)
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for row in cursor:
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if row is None:
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embeddings.append([])
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else:
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rdata = json.loads(row[0])
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# dereference string as array
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vec = json.loads(rdata["embed_vector"])
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embeddings.append(vec)
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cursor.close()
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return embeddings
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@@ -159,20 +165,27 @@ class OracleEmbeddings(BaseModel, Embeddings):
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"""
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# A sample unit test.
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''' get the Oracle connection '''
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import oracledb
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# get the Oracle connection
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conn = oracledb.connect(
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user="",
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password="",
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dsn="")
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user="<user>",
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password="<password>",
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dsn="<hostname>/<service_name>",
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)
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print("Oracle connection is established...")
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''' params '''
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embedder_params = {"provider":"database", "model":"demo_model"}
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# params
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embedder_params = {"provider": "database", "model": "demo_model"}
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proxy = ""
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''' instance '''
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# instance
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embedder = OracleEmbeddings(conn=conn, params=embedder_params, proxy=proxy)
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docs = ["hello world!", "hi everyone!", "greetings!"]
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embeds = embedder.embed_documents(docs)
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print(f"Total Embeddings: {len(embeds)}")
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print(f"Embedding generated by OracleEmbeddings: {embeds[0]}\n")
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embed = embedder.embed_query("Hello World!")
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print(f"Embedding generated by OracleEmbeddings: {embed}")
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