help for how to use llama_index on chroma.[Question]:

[pythonmanGo]

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Question

I'm using llama_index on chroma ,but there is still a question.

According to the example：[Chroma - LlamaIndex 🦙 0.7.22 (gpt-index.readthedocs.io)](https://gpt-index.readthedocs.io/en/stable/examples/vector_stores/ChromaIndexDemo.html#basic-example-using-the-docker-container)

Normally, we delete or modify a document based on our query, not based on the ID of a document, because the document id is an internal field that we cannot know in advance.     But is now based on the code:

''' query_engine = index.as_query_engine() response = query_engine.query("What did the author do growing up?") '''

Now according to the sample code, we can only get the text result of the query, but cannot get the corresponding id of the text, so we cannot delete or update the query result.     How to query the corresponding id of the result text based on prompt, so as to delete or modify the operation later?

`db = chromadb.PersistentClient(path="./chroma_db") chroma_collection = db.get_or_create_collection("datartchromaDB") vector_store = ChromaVectorStore(chroma_collection=chroma_collection) storage_context = StorageContext.from_defaults(vector_store=vector_store)

    index = VectorStoreIndex.from_vector_store(
        vector_store=vector_store,
        storage_context=storage_context,
        service_context=service_context,
    )
   query_engine = index.as_query_engine()

    response = query_engine.query(prompt)

    doc_to_update = chroma_collection.get(limit=1)

how to get the id of the result ( response = query_engine.query(prompt)) ,which i had prompt from gpt3.5

    print(doc_to_update)
        # delete the last document
    print("count before", chroma_collection.count())
    chroma_collection.delete(ids=[doc_to_update["ids"][0]])
    print("count after", chroma_collection.count())`

[dosubot[bot]]

Answer generated by a 🤖

Answer

Hello,

Thank you for your detailed question. Based on the context you've provided, it seems you're trying to retrieve the ID of a document from a query result in order to perform delete or update operations.

In the current version of LlamaIndex (v0.7.23), the query method of the QueryEngine class returns a list of QueryResult objects. Each QueryResult object contains a doc attribute, which is a Document object. The Document object should have an id attribute that you can use for your delete or update operations.

Here's an example of how you might retrieve the ID from a query result:

response = query_engine.query(prompt)
doc_id = response[0].doc.id  # Get the ID of the first document in the results

Once you have the ID, you can perform delete or update operations as follows:

# Delete a document
chroma_collection.delete(ids=[doc_id])

# Update a document
doc_to_update = chroma_collection.get(doc_id)
doc_to_update["field_to_update"] = "new value"
chroma_collection.update(doc_to_update)

Please note that the exact method names and usage may vary depending on the specific implementation of your chroma_collection object.

I hope this helps! If you have any further questions, please don't hesitate to ask.

Sources

• llama_index/indices/base.py
• llama_index/query_engine/citation_query_engine.py

---

This response is meant to be useful, save you time, and share context. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

[pythonmanGo]

`llm=ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo-0301", max_tokens=1024)

llm_predictor = LLMPredictor(llm=llm)

service_context = ServiceContext.from_defaults(

prompt_helper=prompt_helper,

llm_predictor=llm_predictor ,

node_parser=node_parser,

    chunk_size=1024)

set_global_service_context(service_context)

db = chromadb. PersistentClient(path="./chroma_db")

    chroma_collection = db.get_or_create_collection("datartchromaDB")

    vector_store = ChromaVectorStore(chroma_collection=chroma_collection)

    storage_context = StorageContext.from_defaults(vector_store=vector_store)

    index = VectorStoreIndex.from_vector_store(

        vector_store=vector_store,

        storage_context=storage_context,

        service_context=service_context,

    )

    # Query Data from the persisted index

    query_engine = index.as_query_engine()

    response = query_engine.query(prompt)

    Acflag="Delete"

    doc_to_update_id=response.source_nodes[0].node.ref_doc_id

    if         Acflag=="Delete":

        print("begin delse")

        #doc_to_update = chroma_collection.get(limit=1)

        print(doc_to_update)

        # delete the last document

        print("count before", chroma_collection.count())

        chroma_collection.delete(ids=[doc_to_update_id])

        print("count after", chroma_collection.count())

    elif  Acflag=="Update":

            doc_to_update = chroma_collection.get(ids=[doc_to_update_id])

            #new_content = 'new record'

            doc_to_update["documents"][0] = new_content

            chroma_collection.update(

                ids=[doc_to_update_id],

                documents=doc_to_update["documents"]

            )

`

thanks a lot when i update i got a error :

Traceback (most recent call last):

File "C:\java\javaproject\langchainServerXLDB\langchainServerNEWXLDB.py", line 496, in MixSerchGPT

chroma_collection.update(

File "C:\java\anaconda3\envs\langchainChatbot\lib\site-packages\chromadb\api\models\Collection.py", line 276, in update

self._client._update(self.id, ids, embeddings, metadatas, documents)

File "C:\java\anaconda3\envs\langchainChatbot\lib\site-packages\chromadb\api\segment.py", line 267, in _update

self._validate_embedding_record(coll, r)

File "C:\java\anaconda3\envs\langchainChatbot\lib\site-packages\chromadb\api\segment.py", line 522, in _validate_embedding_record

self._validate_dimension(collection, len(record["embedding"]), update=True)

File "C:\java\anaconda3\envs\langchainChatbot\lib\site-packages\chromadb\api\segment.py", line 537, in _validate_dimension

raise InvalidDimensionException(

chromadb.errors.InvalidDimensionException: Embedding dimension 384 does not match collection dimensionality 1536

During handling of the above exception, another exception occurred:

Traceback (most recent call last):

File "C:\java\javaproject\langchainServerXLDB\langchainServerNEWXLDB.py", line 928, in

seems when i update the record the embedding method use default method ,but when i add the record to the chromadb the method is gpt-3.5-turbo-0301 how can i resolve it. maybe we need a method to update chromadb by llama_index.

By the way how add a record to chromadb quikly ,my data is like :

data = { 'ids': ['250bb797-bb3d-4824-8d69-e62125645951'], 'embeddings': None, 'metadatas': [ { 'doc_id': '702ecd1e-9305-4a71-a5fa-7d43f366c72d', 'document_id': '702ecd1e-9305-4a71-a5fa-7d43f366c72d', 'node_info': '{"start": 3428, "end": 4047, "_node_type": "1"}', 'ref_doc_id': '702ecd1e-9305-4a71-a5fa-7d43f366c72d', 'relationships': '{"1": "702ecd1e-9305-4a71-a5fa-7d43f366c72d", "2": "be8ceeda-b69b-48c6-b12a-cc5859dcd488", "3": "9e96142a-0c6d-4138-80f3-544972b38053"}' } ], 'documents': [ 'recorder1........', 'recorder2........', 'recorder3........' ] }

[pythonmanGo]

thanks a lot when i update i got a error :

seems when i update the record the embedding method use default method ,but when i add the record to the chromadb the method is gpt-3.5-turbo-0301 how can i resolve it. maybe we need a method to update chromadb by llama_index.

By the way how add a record to chromadb quikly ,my data is like :

[dosubot[bot]]

Answer generated by a 🤖

Hello,

The error message you're encountering, chromadb.errors.InvalidDimensionException: Embedding dimension 384 does not match collection dimensionality 1536, typically occurs when the dimension of the data you're trying to add to ChromaDB doesn't match the dimension of the existing data in the database.

To resolve this, you need to ensure that the dimension of the embedding results you're trying to add matches the dimension of the existing data in the ChromaDB collection. This can be achieved by passing the correct embedding model to the index initialization function, not the index.query function. This solution was suggested in a similar issue in the LlamaIndex repository: Dimensionality of query embeddings does not match index dimensionality.

As for adding data to ChromaDB using LlamaIndex, you can use the add method of the ChromaVectorStore class. This method takes a list of NodeWithEmbedding objects as input, which contain the data to be added to the ChromaDB collection. Here's an example:

from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.vector_stores.types import NodeWithEmbedding
from llama_index.schema import TextNode

# Assuming you have a ChromaDB collection instance
chroma_collection = ...

# Create a ChromaVectorStore instance
chroma_vector_store = ChromaVectorStore(chroma_collection)

# Create a list of NodeWithEmbedding objects
embedding_results = [
    NodeWithEmbedding(
        id="node1",
        node=TextNode(text="text1"),
        embedding=[0.1, 0.2, 0.3, ...],  # Replace with your actual embedding
    ),
    NodeWithEmbedding(
        id="node2",
        node=TextNode(text="text2"),
        embedding=[0.4, 0.5, 0.6, ...],  # Replace with your actual embedding
    ),
    # Add more NodeWithEmbedding objects as needed
]

# Add the data to the ChromaDB collection
chroma_vector_store.add(embedding_results)

In this example, replace [0.1, 0.2, 0.3, ...] and [0.4, 0.5, 0.6, ...] with your actual embeddings. The embeddings should be lists of floats. The dimension of these embeddings should match the dimension of the existing data in the ChromaDB collection.

Lastly, the default embedding method used by LlamaIndex when updating a record is the OpenAI's text search mode with the model "text-embedding-ada-002". This is evident from the __init__ method of the OpenAIEmbedding class where the default values for mode and model parameters are set to OpenAIEmbeddingMode.TEXT_SEARCH_MODE and OpenAIEmbeddingModelType.TEXT_EMBED_ADA_002 respectively.

I hope this helps! Let me know if you have any other questions.

Sources

• Dimensionality of query embeddings does not match index dimensionality
• llama_index/vector_stores/chroma.py
• llama_index/embeddings/openai.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[pythonmanGo]

@dosu-bot if i need embedding whith chatgpt3.5 ,please modyfied the code for me

[dosubot[bot]]

I am a 🤖

To use the ChromaVectorStore with OpenAI embeddings, you need to follow these steps:

1. Set up OpenAI and ChromaDB:

   
   import os
   import getpass
   import openai
   import chromadb

os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:") openai.api_key = os.environ["OPENAI_API_KEY"]

chroma_client = chromadb.EphemeralClient() chroma_collection = chroma_client.create_collection("quickstart")

2. Import necessary modules from llama_index and set up the embedding model:
```python
from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
from llama_index.vector_stores import ChromaVectorStore
from llama_index.storage.storage_context import StorageContext
from llama_index.embeddings import LangchainEmbedding
from langchain.embeddings.huggingface import HuggingFaceEmbeddings

embed_model = LangchainEmbedding(
    HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
)

3. Load documents:

   documents = SimpleDirectoryReader(
   "../../../examples/paul_graham_essay/data"
   ).load_data()

4. Set up ChromaVectorStore and load in data:

   vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
   storage_context = StorageContext.from_defaults(vector_store=vector_store)
   service_context = ServiceContext.from_defaults(embed_model=embed_model)
   index = VectorStoreIndex.from_documents(
   documents, storage_context=storage_context, service_context=service_context
   )

5. Query Data:

   query_engine = index.as_query_engine()
   response = query_engine.query("What did the author do growing up?")

The ChromaVectorStore in the LlamaIndex framework is a specific type of VectorStore that uses a ChromaDB collection to store embeddings. It is responsible for storing, querying, and deleting embeddings.

The OpenAI embeddings in the LlamaIndex framework are used to generate vector representations of text data. These embeddings are used to measure the similarity between different pieces of text and to perform text search operations. The OpenAI embeddings are generated using different OpenAI models, such as Davinci, Curie, Babbage, Ada, and Text-Embed-Ada-002.

Please note that the OpenAI embeddings are not explicitly mentioned in the context, but they are implicitly used when creating the VectorStoreIndex and the VectorIndexAutoRetriever.

For more details, you can refer to the following sources:

• docs/examples/vector_stores/chroma_auto_retriever.ipynb
• docs/examples/vector_stores/ChromaIndexDemo.ipynb
• llama_index/vector_stores/chroma.py
• llama_index/embeddings/openai.py

I hope this helps! If you have any other questions, feel free to ask.

Sources

• docs/examples/vector_stores/chroma_auto_retriever.ipynb
• docs/examples/vector_stores/ChromaIndexDemo.ipynb
• llama_index/vector_stores/chroma.py
• llama_index/embeddings/openai.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.