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refactor:refactor knowledge api

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1.delete CFG in embedding_engine api
2.add a text_splitter param in embedding_engine api
This commit is contained in:
aries_ckt
2023-07-11 16:33:48 +08:00
parent 6ff7ef9da4
commit e6aa46fc87
24 changed files with 161 additions and 151 deletions
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8
docs/modules/knowledge.rst
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@@ -26,7 +26,7 @@ before execution:
::
url = "https://db-gpt.readthedocs.io/en/latest/getting_started/getting_started.html"
embedding_model = "text2vec"
embedding_model = "your_model_path/all-MiniLM-L6-v2"
vector_store_config = {
"vector_store_name": your_name,
}
@@ -43,9 +43,11 @@ Document type can be .txt, .pdf, .md, .doc, .ppt.
::
document_path = "your_path/test.md"
embedding_model = "text2vec"
embedding_model = "your_model_path/all-MiniLM-L6-v2"
vector_store_config = {
"vector_store_name": your_name,
"vector_store_type": "Chroma",
"chroma_persist_path": "your_persist_dir",
}
embedding_engine = EmbeddingEngine(
knowledge_source=document_path,
@@ -59,7 +61,7 @@ Document type can be .txt, .pdf, .md, .doc, .ppt.
::
raw_text = "a long passage"
embedding_model = "text2vec"
embedding_model = "your_model_path/all-MiniLM-L6-v2"
vector_store_config = {
"vector_store_name": your_name,
}

8
docs/use_cases/knownledge_based_qa.md
View File

@@ -32,11 +32,17 @@ Below is an example of using the knowledge base API to query knowledge:
```
vector_store_config = {
"vector_store_name": name
"vector_store_name": your_name,
"vector_store_type": "Chroma",
"chroma_persist_path": "your_persist_dir",
}
integrate
query = "your query"
embedding_model = "your_model_path/all-MiniLM-L6-v2"
embedding_engine = EmbeddingEngine(knowledge_source=url, knowledge_type=KnowledgeType.URL.value, model_name=embedding_model, vector_store_config=vector_store_config)
embedding_engine.similar_search(query, 10)