Dynamic Vector Store Selection in Flowise Using an API Variable

Below is an explanation based on LangChain's approach to vector stores and indexing. While Flowise isn’t covered explicitly in the LangChain Python docs, you can achieve dynamic vectorstore selection in your own code. The LangChain APIs themselves are built so that all supported vector stores adhere to a common interface (such as having similar methods for document addition, deletion, and searching). This means you can instantiate multiple vector store objects and then choose one based on your API variable.

How You Can Implement It

1. Instantiate Your Vector Stores Separately
   Create and configure each vector store (or collection) that you wish to use. For example, if you have separate FAISS or Chroma instances, instantiate them as usual.

2. Determine the Active Vector Store
   Write custom logic in your code (this could be as simple as an if-else block in Python) that selects the appropriate vector store instance based on the variable passed through the API. Since LangChain’s vector stores follow the same interface (e.g., having similarity_search), switching between them becomes straightforward.

3. Use the Selected Store
   Once you have determined which vector store to use, simply invoke the search or document management methods on that specific vector store instance.

Example Outline

Below is an outline of how you might implement this:

PYTHONCopied
# Assume vector_store_a and vector_store_b are set up according to your requirements
vector_store_a = FAISS.from_texts(["document A1", "document A2"], embeddings)
vector_store_b = FAISS.from_texts(["document B1", "document B2"], embeddings)

def get_vector_store(api_parameter):
    if api_parameter == "option_a":
        return vector_store_a
    elif api_parameter == "option_b":
        return vector_store_b
    else:
        raise ValueError("Unsupported vector store option")

# Suppose your API passes a parameter "store_option"
store_option = "option_a"  # This would come from your API request
selected_vector_store = get_vector_store(store_option)

# Now use the selected vector store for further operations (e.g., query)
results = selected_vector_store.similarity_search("Your query", k=2)
print(results)

Considerations

• Custom Implementation:
  Flowise does not yet provide a built-in if-else or custom JavaScript component to switch vector store collections based on an API variable. Instead, you can implement this selection logic in your own Python code before handing control over to a Flowise-compatible chain or tool.

• Custom Components:
  If you need to integrate more deeply within Flowise, you might wrap this kind of logic in a custom component or function that handles the vector store routing.

• Uniform API:
  Thanks to LangChain’s design, all supported vector stores (like FAISS, Chroma, Pinecone, etc.) have similar methods (e.g., similarity_search, delete, add_documents). This means your routing logic doesn’t have to worry about differences in interface, making dynamic selection feasible.

By implementing this custom routing in your Python code, you can dynamically select the vector store (or collection) based on an API variable even though Flowise’s built-in components might not offer it directly.