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chore: update create chroma store param (#2798)

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This commit is contained in:
alanchen
2025-06-27 18:01:32 +08:00
committed by GitHub
parent bf6f38906d
commit f423b1fb2c
7 changed files with 38 additions and 42 deletions
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10
docs/docs/agents/modules/memory/hybrid_memory.md
View File

@@ -57,12 +57,10 @@ from dbgpt_ext.storage.vector_store.chroma_store import ChromaVectorConfig, Chro
shutil.rmtree("/tmp/tmp_ltm_vector_store", ignore_errors=True)
vector_store = ChromaStore(
ChromaVectorConfig(
embedding_fn=embeddings,
vector_store_config=ChromaVectorConfig(
name="ltm_vector_store",
persist_path="/tmp/tmp_ltm_vector_store",
),
)
persist_path="/tmp/tmp_ltm_vector_store",
),
name="ltm_vector_store",
embedding_fn=embeddings
)
```

12
docs/docs/agents/modules/memory/long_term_memory.md
View File

@@ -47,13 +47,11 @@ from dbgpt_ext.storage.vector_store.chroma_store import ChromaVectorConfig, Chro
# Delete old vector store directory(/tmp/tmp_ltm_vector_stor)
shutil.rmtree("/tmp/tmp_ltm_vector_store", ignore_errors=True)
vector_store = ChromaStore(
ChromaVectorConfig(
embedding_fn=embeddings,
vector_store_config=ChromaVectorConfig(
name="ltm_vector_store",
persist_path="/tmp/tmp_ltm_vector_store",
),
)
vector_store_config=ChromaVectorConfig(
persist_path="/tmp/tmp_ltm_vector_store",
),
name="ltm_vector_store",
embedding_fn=embeddings,
)
```

14
docs/docs/awel/cookbook/first_rag_with_awel.md
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@@ -11,7 +11,7 @@ In this example, we will load your knowledge from a URL and store it in a vector
First, you need to install the `dbgpt` library.
```bash
pip install "dbgpt[rag]>=0.5.2"
pip install "dbgpt[agent,simple_framework, client]>=0.7.1" "dbgpt_ext>=0.7.1" -U
````
### Prepare Embedding Model
@@ -84,10 +84,10 @@ shutil.rmtree("/tmp/awel_rag_test_vector_store", ignore_errors=True)
vector_store = ChromaStore(
vector_store_config=ChromaVectorConfig(
name="test_vstore",
persist_path="/tmp/awel_rag_test_vector_store",
embedding_fn=embeddings
)
persist_path="/tmp/awel_rag_test_vector_store"
),
name="test_vstore",
embedding_fn=embeddings
)
with DAG("load_knowledge_dag") as knowledge_dag:
@@ -274,10 +274,10 @@ shutil.rmtree("/tmp/awel_rag_test_vector_store", ignore_errors=True)
vector_store = ChromaStore(
vector_store_config=ChromaVectorConfig(
name="test_vstore",
persist_path="/tmp/awel_rag_test_vector_store",
embedding_fn=embeddings
),
name="test_vstore",
embedding_fn=embeddings
)
with DAG("load_knowledge_dag") as knowledge_dag:

22
docs/docs/awel/cookbook/write_your_chat_database.md
View File

@@ -29,7 +29,8 @@ In this guide, we mainly focus on step 1, 2, and 3.
First, you need to install the `dbgpt` library.
```bash
pip install "dbgpt[rag]>=0.7.0" -U
pip install "dbgpt[rag, agent, client, simple_framework]>=0.7.0" "dbgpt_ext>=0.7.0" -U
pip install openai
````
## Build Knowledge Base
@@ -92,9 +93,9 @@ shutil.rmtree("/tmp/awel_with_data_vector_store", ignore_errors=True)
vector_store = ChromaStore(
ChromaVectorConfig(
persist_path="/tmp/tmp_ltm_vector_store",
name="ltm_vector_store",
embedding_fn=embeddings,
)
),
name="ltm_vector_store",
embedding_fn=embeddings,
)
with DAG("load_schema_dag") as load_schema_dag:
@@ -102,7 +103,7 @@ with DAG("load_schema_dag") as load_schema_dag:
# Load database schema to vector store
assembler_task = DBSchemaAssemblerOperator(
connector=db_conn,
index_store=vector_store,
table_vector_store_connector=vector_store,
chunk_parameters=ChunkParameters(chunk_strategy="CHUNK_BY_SIZE")
)
input_task >> assembler_task
@@ -122,7 +123,8 @@ with DAG("retrieve_schema_dag") as retrieve_schema_dag:
# Retrieve database schema from vector store
retriever_task = DBSchemaRetrieverOperator(
top_k=1,
index_store=vector_store,
table_vector_store_connector=vector_store,
field_vector_store_connector=vector_store
)
input_task >> retriever_task
@@ -487,10 +489,10 @@ db_conn.create_temp_tables(
vector_store = ChromaStore(
ChromaVectorConfig(
embedding_fn=embeddings,
name="db_schema_vector_store",
persist_path="/tmp/awel_with_data_vector_store",
)
),
embedding_fn=embeddings,
name="db_schema_vector_store",
)
antv_charts = [
@@ -623,7 +625,7 @@ with DAG("load_schema_dag") as load_schema_dag:
# Load database schema to vector store
assembler_task = DBSchemaAssemblerOperator(
connector=db_conn,
index_store=vector_store,
table_vector_store_connector=vector_store,
chunk_parameters=ChunkParameters(chunk_strategy="CHUNK_BY_SIZE"),
)
input_task >> assembler_task