Unexpected err=ValueError('Unknown model: claude-3-opus-20240229. Please provide a valid Anthropic model name.Known models are: claude-instant-1, claude-instant-1.2, claude-2, claude-2.0, claude-2.1'), type(err)=<class 'ValueError'>

client = qdrant_client.QdrantClient(host=QDRANT_HOST,
                                    grpc_port=QDRANT_GRPC_PORT,
                                    prefer_grpc=True,
                                    api_key=QDRANT_API_KEY)
vector_store = QdrantVectorStore(client=client,
                                  collection_name=collection_name,
                                  batch_size=20)
index = VectorStoreIndex.from_vector_store(vector_store=vector_store,
                                           service_context=service_context)
for document in documents:
    try:
        log.info(f"SETUP: Source {source_id} Updating document")
        index.update_ref_doc(
            document,
            update_kwargs={
                "delete_kwargs": {
                    "delete_from_docstore": True
                }
            },
        )
    except Exception as err:
        log.info(f"SETUP: Source {source_id} Error: {err}")
        log.info(f"SETUP: Source {source_id} Update failed, trying insert")
        index.insert(document)

node_postprocessors=[
            MetadataReplacementPostProcessor(target_metadata_key="window"),
            cohere_rerank
 ]

node_postprocessors=[
            cohere_rerank,
            MetadataReplacementPostProcessor(target_metadata_key="window")
]

ERROR: Could not find a version that satisfies the requirement llama-index-llms-gemini (from versions: none)
ERROR: No matching distribution found for llama-index-llms-gemini

node_parser = SentenceWindowNodeParser.from_defaults(
    window_size=3,
    window_metadata_key="window",
    original_text_metadata_key="original_text",
)

service_context = ServiceContext.from_defaults(llm=llm,
                                               node_parser=node_parser,
                                               embed_model=embed_model)
index = VectorStoreIndex.from_vector_store(vector_store=vector_store,
                                           service_context=service_context)
chat_engine = index.as_chat_engine(
    similarity_top_k=2,
    # the target key defaults to `window` to match the node_parser's default
    node_postprocessors=[
        MetadataReplacementPostProcessor(target_metadata_key="window")
    ],
    vector_store_kwargs={"qdrant_filters": filters})

node_postprocessors=[
    MetadataReplacementPostProcessor(target_metadata_key="window")
],

MODEL = "gpt-4-1106-preview"
EMBED_MODEL = "text-embedding-3-large"
llm = OpenAI(model=MODEL, temperature=0.1)
node_parser = SentenceWindowNodeParser.from_defaults(
    window_size=3,
    window_metadata_key="window",
   original_text_metadata_key="original_text",
)
embed_model = OpenAIEmbedding()
client = qdrant_client.QdrantClient(QDRANT_URL, api_key=QDRANT_API_KEY)

pdf_reader = SimpleDirectoryReader(input_files=pdf_files)
documents = pdf_reader.load_data()
vector_store = QdrantVectorStore(client=client,            collection_name=collection_name,                  batch_size=20)
service_context = ServiceContext.from_defaults(llm=llm,                      node_parser=node_parser,                          embed_model=embed_model)
index = VectorStoreIndex.from_vector_store(vector_store=vector_store,                         service_context=service_context)
refreshed_docs = index.refresh_ref_docs(documents)

WARNING - Retrying llama_index.embeddings.openai.get_embeddings in 1.6310027891256675 seconds as it raised BadRequestError: Error code: 400 - {'error': {'message': "This model's maximum context length is 8192 tokens, however you requested 8212 tokens (8212 in your prompt; 0 for the completion). Please reduce your prompt or completion length.", 'type': 'invalid_request_error', 'param': None, 'code': None}}.

index = VectorStoreIndex.from_vector_store(vector_store=vector_store, service_context=service_context)
refreshed_docs = index.refresh_ref_docs(
    documents,
    update_kwargs={"delete_kwargs": {
        "delete_from_docstore": True
    }}
)

document.metadata = {
    "source_id": source_id,
    "document_name": document_name
}

retriever = index.as_retriever(...)
retrieved_nodes = retriever.retrieve(query)

chat_engine = index.as_chat_engine(
        similarity_top_k=similarity_top_k,
        node_postprocessors=node_postprocessors,
        vector_store_kwargs={"qdrant_filters": filters})

node_parser = SentenceWindowNodeParser.from_defaults(
    window_size=3,
    window_metadata_key="window",
   original_text_metadata_key="original_text",
)
embed_model = OpenAIEmbedding(embed_batch_size=100)
client = qdrant_client.QdrantClient(QDRANT_URL, api_key=QDRANT_API_KEY)

loader = JsonDataReader()
document = loader.load_data(json_string)
vector_store = QdrantVectorStore(client=client,                      collection_name=collection_name)
service_context = ServiceContext.from_defaults(llm=llm,                                      node_parser=node_parser,
                                             embed_model=embed_model)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(documents,                                    storage_context=storage_context,                                      service_context=service_context)

ERROR - Unexpected err=ResponseHandlingException(WriteTimeout('The write operation timed out')), type(err)=<class 'qdrant_client.http.exceptions.ResponseHandlingException'>

embed_model = OpenAIEmbedding(embed_batch_size=100)